Here I use the CPT 2007 data set to illustrate new options avialable for ST Treatment Stability/Trial Dendrogram plots.

Multilocation

Using scripts from ARM ST to show options.

cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
cbColors <- c(cbPalette,cbbPalette)
path = "../Manuscripts/scripts/multilocation"
means.vector <- read.delim('../Manuscripts/scripts/multilocation/trialMeans.SmyCol1.tab',header=FALSE)
means.matrix <- read.delim('../Manuscripts/scripts/multilocation/trialTable.SmyCol1.tab',header=FALSE)
means.vector <- means.vector[,1]
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 6, 2, 1) + 0.1), ps = 12, cex.lab = 1.166667, cex.main = 1.333333, cex.axis = 1)
res1<-plot.interaction.ARMST(means.matrix, means.vector, ylab='Treatment in Trial Mean \nYield',regression=TRUE, main='Treatment Stability and Trial Clusters for Grand Mean 1', show.legend=TRUE,legend.columns=1, legend.pos=c(.01,.98),trt.colors=cbColors)
par(fig=c(0,1,0,.4),mar=(c(4, 6, 0, 1) + 0.1), new=TRUE)
res2<-plot.clusters.ARMST(means.matrix, means.vector, xlab='Trial Mean \nMultilocation', ylab='',trt.colors =cbColors)

par(fig = c(0, 1, 0, 1))
path = "../Manuscripts/scripts/multilocation"
means.vector <- read.delim('../Manuscripts/scripts/multilocation/trialMeans.SmyCol1.tab',header=FALSE)
means.matrix <- read.delim('../Manuscripts/scripts/multilocation/trialTable.SmyCol1.tab',header=FALSE)
means.vector <- means.vector[,1]
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 6, 2, 1) + 0.1), ps = 12, cex.lab = 1.166667, cex.main = 1.333333, cex.axis = 1)
res1<-plot.interaction.ARMST(means.matrix, means.vector, ylab='Treatment in Trial Mean \nYield',regression=TRUE, main='Treatment Stability and Trial Clusters for Grand Mean 1', show.legend=TRUE,legend.columns=1, legend.pos=c(.01,.98),trt.colors=cbColors)
par(fig=c(0,1,0,.4),mar=(c(4, 6, 0, 1) + 0.1), new=TRUE)
decomp <- decompose.means.table(means.matrix)
fg="black"
res2<-plot.clusters.ARMST(means.matrix, means.vector, fg=fg,xlab='Trial Mean \nMultilocation', ylab='',reference=(decomp$mu + decomp$alpha + decomp$beta),trt.colors=cbColors)
fg=cbColors[2]

res3 <- plot.clusters.ARMST(decomp$mu + decomp$alpha + decomp$beta, means.vector, fg=fg,add=TRUE,xlab='Trial Mean \nMultilocation', ylab='',trt.colors=cbColors)

par(fig = c(0, 1, 0, 1))
str(res2$means.hc)
## List of 7
##  $ merge      : int [1:8, 1:2] -6 -1 -5 -4 2 -2 1 6 -8 -3 ...
##  $ height     : num [1:8] 0.168 0.227 0.385 0.423 0.507 ...
##  $ order      : int [1:9] 2 4 9 6 8 1 3 5 7
##  $ labels     : NULL
##  $ method     : chr "complete"
##  $ call       : language hclust(d = dist(means.matrix), method = method)
##  $ dist.method: chr "euclidean"
##  - attr(*, "class")= chr "hclust"
res2$means.hc$height
## [1] 0.1680609 0.2272786 0.3849098 0.4228869 0.5068531 0.5913357 0.9790983
## [8] 1.9802638
res3$means.hc$height
## [1] 0.05000015 0.05500003 0.10166665 0.12000000 0.42166670 0.44333339
## [7] 0.96833340 1.94666672
res2$means.hc$height/res3$means.hc$height
## [1] 3.361208 4.132335 3.785999 3.524058 1.202023 1.333840 1.011117 1.017259
res2$means.hc$order
## [1] 2 4 9 6 8 1 3 5 7
res3$means.hc$order
## [1] 2 4 9 6 8 5 7 1 3
res2$means.hc$merge
##      [,1] [,2]
## [1,]   -6   -8
## [2,]   -1   -3
## [3,]   -5   -7
## [4,]   -4   -9
## [5,]    2    3
## [6,]   -2    4
## [7,]    1    5
## [8,]    6    7
res3$means.hc$merge
##      [,1] [,2]
## [1,]   -5   -7
## [2,]   -4   -9
## [3,]   -1   -3
## [4,]   -6   -8
## [5,]    1    3
## [6,]   -2    2
## [7,]    4    5
## [8,]    6    7
res2$means.hc$merge==res3$means.hc$merge
##       [,1]  [,2]
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
## [4,] FALSE FALSE
## [5,] FALSE  TRUE
## [6,]  TRUE FALSE
## [7,] FALSE  TRUE
## [8,]  TRUE  TRUE
matched.idx <- compare.merges(res2$means.hc$merge,res3$means.hc$merge)
matched.idx
## [1] 4 3 1 2 0 0 0 8
res2$means.hc$height
## [1] 0.1680609 0.2272786 0.3849098 0.4228869 0.5068531 0.5913357 0.9790983
## [8] 1.9802638
res3$means.hc$height
## [1] 0.05000015 0.05500003 0.10166665 0.12000000 0.42166670 0.44333339
## [7] 0.96833340 1.94666672
res3$means.hc$height[matched.idx]
## [1] 0.12000000 0.10166665 0.05000015 0.05500003 1.94666672

The function cluster.stats() in the fpc package provides a mechanism for comparing the similarity of two cluster solutions using a variety of validation criteria (Hubert’s gamma coefficient, the Dunn index and the corrected rand index) # comparing 2 cluster solutions

library(fpc)
d <- dist(means.matrix)
cluster.stats(d, res2$clusters, res3$clusters)
## $n
## [1] 9
## 
## $cluster.number
## [1] 3
## 
## $cluster.size
## [1] 4 3 2
## 
## $min.cluster.size
## [1] 2
## 
## $noisen
## [1] 0
## 
## $diameter
## [1] 0.5068531 0.5913357 0.1680609
## 
## $average.distance
## [1] 0.4002332 0.5152329 0.1680609
## 
## $median.distance
## [1] 0.4055768 0.5314759 0.1680609
## 
## $separation
## [1] 0.5229192 0.5952870 0.5229192
## 
## $average.toother
## [1] 0.8850877 1.1943831 1.1381486
## 
## $separation.matrix
##           [,1]     [,2]      [,3]
## [1,] 0.0000000 0.595287 0.5229192
## [2,] 0.5952870 0.000000 1.4083008
## [3,] 0.5229192 1.408301 0.0000000
## 
## $ave.between.matrix
##           [,1]      [,2]      [,3]
## [1,] 0.0000000 0.9694404 0.7585586
## [2,] 0.9694404 0.0000000 1.6442685
## [3,] 0.7585586 1.6442685 0.0000000
## 
## $average.between
## [1] 1.060283
## 
## $average.within
## [1] 0.4115159
## 
## $n.between
## [1] 26
## 
## $n.within
## [1] 10
## 
## $max.diameter
## [1] 0.5913357
## 
## $min.separation
## [1] 0.5229192
## 
## $within.cluster.ss
## [1] 0.5365621
## 
## $clus.avg.silwidths
##         1         2         3 
## 0.4281916 0.4560306 0.7772011 
## 
## $avg.silwidth
## [1] 0.5150289
## 
## $g2
## NULL
## 
## $g3
## NULL
## 
## $pearsongamma
## [1] 0.646884
## 
## $dunn
## [1] 0.8843018
## 
## $dunn2
## [1] 1.472264
## 
## $entropy
## [1] 1.060857
## 
## $wb.ratio
## [1] 0.3881188
## 
## $ch
## [1] 18.8348
## 
## $cwidegap
## [1] 0.3865230 0.5314759 0.1680609
## 
## $widestgap
## [1] 0.5314759
## 
## $sindex
## [1] 0.5229192
## 
## $corrected.rand
## [1] 1
## 
## $vi
## [1] 0

where d is a distance matrix among objects, and fit1\(cluster and fit\)cluster are integer vectors containing classification results from two different clusterings of the same data.

library(SASmixed)
data(Multilocation)
mixed.res <- standard.sensitivity.plot(Multilocation,
                                     response = "Adj",
                          TreatmentName = "Trt",
                          TrialName = "Location",
                          RepName="Block",
                          dual.dendrogram=TRUE,
                          plot.outliers=TRUE,legend.columns=1)
## Loading required package: lme4
## Loading required package: Matrix
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?

print.stdplot(mixed.res)
## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
## 
## Response: Adj
##                Df  Sum Sq Mean Sq F value    Pr(>F)    
## Location        8 11.4635 1.43294 41.4400 < 2.2e-16 ***
## Trt             3  1.2217 0.40725 11.7774 4.803e-06 ***
## Location:Trt   24  0.9966 0.04152  1.2008   0.28285    
## Location:Block 18  1.0270 0.05706  1.6500   0.07994 .  
## Residuals      54  1.8672 0.03458                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula: Adj ~ Location + (1 | Location/Block) + (1 | Location:Trt)
##    Data: plot.dat
## 
## REML criterion at convergence: 2.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6168 -0.6321  0.0162  0.5230  2.8392 
## 
## Random effects:
##  Groups         Name        Variance Std.Dev.
##  Location:Trt   (Intercept) 0.015860 0.12594 
##  Block:Location (Intercept) 0.005619 0.07496 
##  Location       (Intercept) 0.028356 0.16839 
##  Residual                   0.034579 0.18595 
## Number of obs: 108, groups:  
## Location:Trt, 36; Block:Location, 27; Location, 9
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)  2.99968    0.19255  15.579
## LocationB   -0.70262    0.27231  -2.580
## LocationC    0.04893    0.27231   0.180
## LocationD   -0.44917    0.27231  -1.649
## LocationE   -0.15282    0.27231  -0.561
## LocationF    0.25891    0.27231   0.951
## LocationG   -0.15720    0.27231  -0.577
## LocationH    0.32281    0.27231   1.185
## LocationI   -0.47818    0.27231  -1.756
## 
## Correlation of Fixed Effects:
##           (Intr) LoctnB LoctnC LoctnD LoctnE LoctnF LoctnG LoctnH
## LocationB -0.707                                                 
## LocationC -0.707  0.500                                          
## LocationD -0.707  0.500  0.500                                   
## LocationE -0.707  0.500  0.500  0.500                            
## LocationF -0.707  0.500  0.500  0.500  0.500                     
## LocationG -0.707  0.500  0.500  0.500  0.500  0.500              
## LocationH -0.707  0.500  0.500  0.500  0.500  0.500  0.500       
## LocationI -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## convergence code: 0
## Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
## 
## [1] 
## [1] Stability
## [1] ----------------------------------------------------
##   Treatment     Slope   Intercept     Mean        SD            b
## 1         1 1.0681951 -0.12482441 2.924011 0.3852070  0.068195097
## 2         2 0.9601752 -0.06288138 2.677644 0.3403034 -0.039824803
## 3         3 1.0084981  0.07100300 2.949452 0.3584278  0.008498139
## 4         4 0.9631316  0.11670279 2.865667 0.3556809 -0.036868433
##          Pb         bR2
## 1 0.5890136 0.043775237
## 2 0.6447955 0.032068736
## 3 0.9287174 0.001226858
## 4 0.7959259 0.010207069
## [1] 
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = as.formula(modelString), data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.37605 -0.12428 -0.01798  0.11932  0.43786 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.245e+00  1.015e-01  31.974  < 2e-16 ***
## Trt2             -2.464e-01  5.240e-02  -4.702 1.11e-05 ***
## Trt3              2.544e-02  5.240e-02   0.486 0.628700    
## Trt4             -5.834e-02  5.240e-02  -1.113 0.268991    
## LocationB        -9.411e-01  1.361e-01  -6.912 1.22e-09 ***
## LocationC        -5.245e-02  1.361e-01  -0.385 0.701107    
## LocationD        -7.574e-01  1.361e-01  -5.563 3.69e-07 ***
## LocationE        -4.590e-01  1.361e-01  -3.371 0.001172 ** 
## LocationF        -2.723e-02  1.361e-01  -0.200 0.842027    
## LocationG        -2.003e-01  1.361e-01  -1.471 0.145247    
## LocationH         1.762e-01  1.361e-01   1.294 0.199515    
## LocationI        -4.800e-01  1.361e-01  -3.526 0.000715 ***
## LocationA:Block2 -2.216e-01  1.361e-01  -1.628 0.107671    
## LocationB:Block2  1.214e-01  1.361e-01   0.892 0.375421    
## LocationC:Block2 -1.820e-01  1.361e-01  -1.337 0.185092    
## LocationD:Block2  2.832e-01  1.361e-01   2.080 0.040818 *  
## LocationE:Block2  1.871e-01  1.361e-01   1.374 0.173392    
## LocationF:Block2  9.575e-02  1.361e-01   0.703 0.483984    
## LocationG:Block2 -2.384e-01  1.361e-01  -1.751 0.083845 .  
## LocationH:Block2 -8.525e-02  1.361e-01  -0.626 0.533046    
## LocationI:Block2 -3.204e-01  1.361e-01  -2.353 0.021173 *  
## LocationA:Block3 -3.036e-01  1.361e-01  -2.230 0.028684 *  
## LocationB:Block3  6.885e-02  1.361e-01   0.506 0.614496    
## LocationC:Block3 -3.895e-02  1.361e-01  -0.286 0.775571    
## LocationD:Block3  1.162e-01  1.361e-01   0.853 0.396119    
## LocationE:Block3  2.062e-01  1.361e-01   1.515 0.133876    
## LocationF:Block3  2.375e-01  1.361e-01   1.745 0.085062 .  
## LocationG:Block3 -1.573e-01  1.361e-01  -1.156 0.251420    
## LocationH:Block3  4.854e-18  1.361e-01   0.000 1.000000    
## LocationI:Block3 -1.993e-01  1.361e-01  -1.464 0.147188    
## eTrt:eLocation    2.473e-01  4.894e-01   0.505 0.614745    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1925 on 77 degrees of freedom
## Multiple R-squared:  0.8278, Adjusted R-squared:  0.7607 
## F-statistic: 12.34 on 30 and 77 DF,  p-value: < 2.2e-16
## 
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## Trt             3  1.222  0.4072  10.986 4.39e-06 ***
## Location        8 11.464  1.4329  38.656  < 2e-16 ***
## Location:Block 18  1.027  0.0571   1.539   0.0995 .  
## eTrt:eLocation  1  0.009  0.0095   0.255   0.6147    
## Residuals      77  2.854  0.0371                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.33457 -0.12065 -0.02561  0.12390  0.40986 
## 
## Coefficients: (1 not defined because of singularities)
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.207e+00  1.049e-01  30.573  < 2e-16 ***
## Trt2             -2.464e-01  5.229e-02  -4.712 1.11e-05 ***
## Trt3              2.544e-02  5.229e-02   0.487  0.62799    
## Trt4             -5.834e-02  5.229e-02  -1.116  0.26805    
## LocationB        -8.252e-01  1.601e-01  -5.155 1.99e-06 ***
## LocationC        -4.599e-02  1.359e-01  -0.338  0.73606    
## LocationD        -6.641e-01  1.520e-01  -4.370 3.94e-05 ***
## LocationE        -4.024e-01  1.420e-01  -2.834  0.00590 ** 
## LocationF        -2.387e-02  1.359e-01  -0.176  0.86101    
## LocationG        -1.756e-01  1.370e-01  -1.282  0.20387    
## LocationH         1.545e-01  1.368e-01   1.129  0.26230    
## LocationI        -4.209e-01  1.425e-01  -2.953  0.00421 ** 
## LocationA:Block2 -2.216e-01  1.358e-01  -1.631  0.10703    
## LocationB:Block2  1.214e-01  1.358e-01   0.893  0.37446    
## LocationC:Block2 -1.820e-01  1.358e-01  -1.340  0.18425    
## LocationD:Block2  2.832e-01  1.358e-01   2.085  0.04048 *  
## LocationE:Block2  1.871e-01  1.358e-01   1.377  0.17257    
## LocationF:Block2  9.575e-02  1.358e-01   0.705  0.48309    
## LocationG:Block2 -2.384e-01  1.358e-01  -1.755  0.08329 .  
## LocationH:Block2 -8.525e-02  1.358e-01  -0.628  0.53221    
## LocationI:Block2 -3.204e-01  1.358e-01  -2.358  0.02097 *  
## LocationA:Block3 -3.036e-01  1.358e-01  -2.235  0.02843 *  
## LocationB:Block3  6.885e-02  1.358e-01   0.507  0.61377    
## LocationC:Block3 -3.895e-02  1.358e-01  -0.287  0.77512    
## LocationD:Block3  1.162e-01  1.358e-01   0.855  0.39517    
## LocationE:Block3  2.062e-01  1.358e-01   1.518  0.13315    
## LocationF:Block3  2.375e-01  1.358e-01   1.748  0.08450 .  
## LocationG:Block3 -1.573e-01  1.358e-01  -1.158  0.25049    
## LocationH:Block3  4.416e-18  1.358e-01   0.000  1.00000    
## LocationI:Block3 -1.993e-01  1.358e-01  -1.467  0.14643    
## Trt1:eLocation    2.321e-01  1.469e-01   1.580  0.11840    
## Trt2:eLocation    1.098e-01  1.469e-01   0.747  0.45725    
## Trt3:eLocation    1.509e-01  1.469e-01   1.027  0.30776    
## Trt4:eLocation           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1921 on 75 degrees of freedom
## Multiple R-squared:  0.833,  Adjusted R-squared:  0.7618 
## F-statistic: 11.69 on 32 and 75 DF,  p-value: < 2.2e-16
## 
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.53048 -0.13666  0.00839  0.13721  0.53582 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## Trt1            2.92401    0.04358  67.092  < 2e-16 ***
## Trt2            2.67764    0.04358  61.439  < 2e-16 ***
## Trt3            2.94945    0.04358  67.676  < 2e-16 ***
## Trt4            2.86567    0.04358  65.753  < 2e-16 ***
## Trt1:eLocation  0.96942    0.12246   7.917 3.43e-12 ***
## Trt2:eLocation  0.84714    0.12246   6.918 4.42e-10 ***
## Trt3:eLocation  0.88822    0.12246   7.253 8.81e-11 ***
## Trt4:eLocation  0.73736    0.12246   6.021 2.88e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2265 on 100 degrees of freedom
## Multiple R-squared:  0.9943, Adjusted R-squared:  0.9938 
## F-statistic:  2172 on 8 and 100 DF,  p-value: < 2.2e-16
## 
##                Df Sum Sq Mean Sq F value Pr(>F)    
## Trt             4  881.0  220.26 4294.92 <2e-16 ***
## Trt:eLocation   4   10.2    2.56   49.85 <2e-16 ***
## Residuals     100    5.1    0.05                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## Trt             3  1.222  0.4072  11.034 4.42e-06 ***
## Location        8 11.464  1.4329  38.824  < 2e-16 ***
## Location:Block 18  1.027  0.0571   1.546   0.0981 .  
## Trt:eLocation   3  0.096  0.0319   0.864   0.4636    
## Residuals      75  2.768  0.0369                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 0.5578604
## [1] Interaction sd Value:
## [1] 0.1201781
## [1] Error sd Value:
## [1] 0.1859535
## [1] Pairs:
## list()
## [1] 
## [1]
response = "Plot.Mean"
TreatmentName = "Criteria.Entry.No."
TrialName = "Expt.No."
RepName="Rep.No."
#yield.desc ="Yield"
#tw.desc="Test Weight"
#hd.desc="Heading"
#ht.desc="Test Weight"
#cpt.dat <- read.csv("CPT.full.subset.csv",header=TRUE)
#cpt.dat <- read.csv("CPT_2007Subsetb.csv",header=TRUE)


yield.desc ="GY"
tw.desc ="TW"
hd.desc="HD"
ht.desc="HT"
cpt.dat <- read.delim("CPT_2007Subset.txt",header=TRUE)
cpt.dat <- subset(cpt.dat,!is.na(cpt.dat$Plot.Mean))
cpt.dat$Expt.No. <- as.factor(cpt.dat$Expt.No.)
TrtNames <- as.character(cpt.dat$Criteria.Entry.No.)
TrtNames[cpt.dat$Criteria.Entry.No.<10] <- paste("0",TrtNames[cpt.dat$Criteria.Entry.No.<10],sep="")
cpt.dat$Criteria.Entry.No. <- as.factor(TrtNames)
cpt.dat$Rep.No. <- as.factor(cpt.dat$Rep.No.)
gy.dat <- subset(cpt.dat,cpt.dat$Description==yield.desc)
tw.dat <- subset(cpt.dat,cpt.dat$Description==tw.desc)
hd.dat <- subset(cpt.dat,cpt.dat$Description==hd.desc)
ht.dat <- subset(cpt.dat,cpt.dat$Description==ht.desc)

gy.dat$Expt.No. <- as.factor(as.character(gy.dat$Expt.No.))
tw.dat$Expt.No. <- as.factor(as.character(tw.dat$Expt.No.))
hd.dat$Expt.No. <- as.factor(as.character(hd.dat$Expt.No.))
ht.dat$Expt.No. <- as.factor(as.character(ht.dat$Expt.No.))
gy.means <- gei.table.and.effects(gy.dat,
                                     response = response,
                          TreatmentName = TreatmentName,
                          TrialName = TrialName,
                          RepName=RepName)
gy.means$trial.means
##        [,1]
## 1  53.67708
## 2  55.57483
## 3  57.78769
## 4  39.01086
## 5  39.54350
## 6  33.59267
## 7  63.30659
## 8  67.83786
## 9  62.65739
## 10 26.95339
## 11 41.62739
## 12 39.34495
## 13 45.65942
colMeans(gy.means$means.table)
##  [1] 53.67708 45.76792 56.31240 52.48333 44.91626 34.96458 55.53854
##  [8] 58.18740 54.31667 30.64583 47.12352 48.92969 43.71042
gy.res <- standard.sensitivity.plot(gy.dat,
                                     response = response,
                          TreatmentName = TreatmentName,
                          TrialName = TrialName,
                          RepName=RepName,
                          dual.dendrogram=FALSE,
                          plot.outliers=TRUE,legend.columns=3)

gy.res <- standard.sensitivity.plot(gy.dat,
                                     response = response,
                          TreatmentName = TreatmentName,
                          TrialName = TrialName,
                          RepName=RepName,
                          outliers=2.4,
                          dual.dendrogram=TRUE,
                          plot.outliers=TRUE,legend.columns=3)

print.stdplot(gy.res)
## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
## 
## Response: Plot.Mean
##                              Df Sum Sq Mean Sq  F value    Pr(>F)    
## Expt.No.                     12  39698  3308.2 194.3594 < 2.2e-16 ***
## Criteria.Entry.No.           11  17509  1591.7  93.5136 < 2.2e-16 ***
## Expt.No.:Criteria.Entry.No. 132  19964   151.2   8.8856 < 2.2e-16 ***
## Expt.No.:Rep.No.             39   3998   102.5   6.0232 < 2.2e-16 ***
## Residuals                   429   7302    17.0                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula: 
## Plot.Mean ~ Expt.No. + (1 | Expt.No./Rep.No.) + (1 | Expt.No.:Criteria.Entry.No.)
##    Data: plot.dat
## 
## REML criterion at convergence: 3977.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.95766 -0.49670 -0.00515  0.49975  2.82744 
## 
## Random effects:
##  Groups                      Name        Variance Std.Dev.
##  Expt.No.:Criteria.Entry.No. (Intercept) 61.256   7.827   
##  Rep.No.:Expt.No.            (Intercept)  7.125   2.669   
##  Expt.No.                    (Intercept) 32.377   5.690   
##  Residual                                17.021   4.126   
## Number of obs: 624, groups:  
## Expt.No.:Criteria.Entry.No., 156; Rep.No.:Expt.No., 52; Expt.No., 13
## 
## Fixed effects:
##                            Estimate Std. Error t value
## (Intercept)                 53.6771     6.2942   8.528
## Expt.No.CPT:2007:Brookings  -7.9092     8.9014  -0.889
## Expt.No.CPT:2007:DLakesPea   2.6353     8.9014   0.296
## Expt.No.CPT:2007:Hayes      -1.1937     8.9014  -0.134
## Expt.No.CPT:2007:Kennebec   -8.7608     8.9014  -0.984
## Expt.No.CPT:2007:Martin    -18.7125     8.9014  -2.102
## Expt.No.CPT:2007:Onida       1.8615     8.9014   0.209
## Expt.No.CPT:2007:Platte      4.5103     8.9014   0.507
## Expt.No.CPT:2007:Selby       0.6396     8.9014   0.072
## Expt.No.CPT:2007:Sturgis   -23.0312     8.9014  -2.587
## Expt.No.CPT:2007:Wall       -6.5536     8.9014  -0.736
## Expt.No.CPT:2007:Watertown  -4.7474     8.9014  -0.533
## Expt.No.CPT:2007:Winner     -9.9667     8.9014  -1.120
## 
## Correlation of Fixed Effects:
##                    (Intr) E.N.CPT:2007:B E.N.CPT:2007:D E.N.CPT:2007:H
## E.N.CPT:2007:B     -0.707                                             
## E.N.CPT:2007:D     -0.707  0.500                                      
## E.N.CPT:2007:H     -0.707  0.500          0.500                       
## E.N.CPT:2007:K     -0.707  0.500          0.500          0.500        
## E.N.CPT:2007:M     -0.707  0.500          0.500          0.500        
## E.N.CPT:2007:O     -0.707  0.500          0.500          0.500        
## E.N.CPT:2007:P     -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:Sl -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:St -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wl -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wt -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wn -0.707  0.500          0.500          0.500        
##                    E.N.CPT:2007:K E.N.CPT:2007:M E.N.CPT:2007:O
## E.N.CPT:2007:B                                                 
## E.N.CPT:2007:D                                                 
## E.N.CPT:2007:H                                                 
## E.N.CPT:2007:K                                                 
## E.N.CPT:2007:M      0.500                                      
## E.N.CPT:2007:O      0.500          0.500                       
## E.N.CPT:2007:P      0.500          0.500          0.500        
## Expt.N.CPT:2007:Sl  0.500          0.500          0.500        
## Expt.N.CPT:2007:St  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wl  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wt  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wn  0.500          0.500          0.500        
##                    E.N.CPT:2007:P Expt.N.CPT:2007:Sl Expt.N.CPT:2007:St
## E.N.CPT:2007:B                                                         
## E.N.CPT:2007:D                                                         
## E.N.CPT:2007:H                                                         
## E.N.CPT:2007:K                                                         
## E.N.CPT:2007:M                                                         
## E.N.CPT:2007:O                                                         
## E.N.CPT:2007:P                                                         
## Expt.N.CPT:2007:Sl  0.500                                              
## Expt.N.CPT:2007:St  0.500          0.500                               
## Expt.N.CPT:2007:Wl  0.500          0.500              0.500            
## Expt.N.CPT:2007:Wt  0.500          0.500              0.500            
## Expt.N.CPT:2007:Wn  0.500          0.500              0.500            
##                    Expt.N.CPT:2007:Wl Expt.N.CPT:2007:Wt
## E.N.CPT:2007:B                                          
## E.N.CPT:2007:D                                          
## E.N.CPT:2007:H                                          
## E.N.CPT:2007:K                                          
## E.N.CPT:2007:M                                          
## E.N.CPT:2007:O                                          
## E.N.CPT:2007:P                                          
## Expt.N.CPT:2007:Sl                                      
## Expt.N.CPT:2007:St                                      
## Expt.N.CPT:2007:Wl                                      
## Expt.N.CPT:2007:Wt  0.500                               
## Expt.N.CPT:2007:Wn  0.500              0.500            
## [1] 
## [1] Stability
## [1] ----------------------------------------------------
##    Treatment     Slope   Intercept     Mean        SD            b
## 1          1 1.4470667 -19.3059998 50.43968 13.881179  0.447066711
## 2          2 1.1321911  -6.2009466 48.36837 10.210409  0.132191105
## 3          3 0.6213510  15.2685346 45.21639  8.308268 -0.378649035
## 4          4 0.5836777   7.1322629 35.26434  9.429917 -0.416322288
## 5          5 1.2073912  -3.8692090 54.32460 12.212059  0.207391158
## 6          6 1.2629921  -4.0818885 56.79177 12.518736  0.262992078
## 7          7 0.9908940  -1.5992298 46.15985  9.914119 -0.009105978
## 8          8 0.7606544   6.5102431 43.17224  7.677330 -0.239345577
## 9          9 0.9801060   0.4427417 47.68186  8.695932 -0.019894018
## 10        10 1.0053054   2.5710136 51.02470  8.950710  0.005305443
## 11        11 0.9178223   5.4363092 49.67348  8.668054 -0.082177704
## 12        12 1.0905481  -2.3038313 50.25838  9.701141  0.090548106
##           Pb          bR2
## 1  0.1044094 0.2216659526
## 2  0.3810162 0.0703845599
## 3  0.1377338 0.1889368944
## 4  0.1841813 0.1543568700
## 5  0.4304031 0.0574180092
## 6  0.3124248 0.0924748910
## 7  0.9646718 0.0001866071
## 8  0.1593920 0.1715657615
## 9  0.8615537 0.0028891132
## 10 0.9647863 0.0001853985
## 11 0.5949401 0.0265318385
## 12 0.4892785 0.0444565619
## [1] 
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = as.formula(modelString), data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -23.2628  -3.9778   0.1832   3.8793  20.6651 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)                          57.408376   2.169661  26.460  < 2e-16
## Criteria.Entry.No.02                 -2.071312   1.339144  -1.547 0.122490
## Criteria.Entry.No.03                 -5.223290   1.339144  -3.900 0.000108
## Criteria.Entry.No.04                -15.175336   1.339144 -11.332  < 2e-16
## Criteria.Entry.No.05                  3.884915   1.339144   2.901 0.003865
## Criteria.Entry.No.06                  6.352087   1.339144   4.743 2.67e-06
## Criteria.Entry.No.07                 -4.279829   1.339144  -3.196 0.001472
## Criteria.Entry.No.08                 -7.267437   1.339144  -5.427 8.55e-08
## Criteria.Entry.No.09                 -2.757819   1.339144  -2.059 0.039919
## Criteria.Entry.No.10                  0.585016   1.339144   0.437 0.662382
## Criteria.Entry.No.11                 -0.766198   1.339144  -0.572 0.567446
## Criteria.Entry.No.12                 -0.181305   1.339144  -0.135 0.892353
## Expt.No.CPT:2007:Brookings           -7.235833   2.787650  -2.596 0.009688
## Expt.No.CPT:2007:DLakesPea            2.829582   2.787650   1.015 0.310524
## Expt.No.CPT:2007:Hayes               -7.083334   2.787650  -2.541 0.011323
## Expt.No.CPT:2007:Kennebec           -11.287547   2.787650  -4.049 5.87e-05
## Expt.No.CPT:2007:Martin             -21.766667   2.787650  -7.808 2.87e-14
## Expt.No.CPT:2007:Onida               -1.084167   2.787650  -0.389 0.697485
## Expt.No.CPT:2007:Platte               4.754999   2.787650   1.706 0.088611
## Expt.No.CPT:2007:Selby               -2.528334   2.787650  -0.907 0.364809
## Expt.No.CPT:2007:Sturgis            -24.325000   2.787650  -8.726  < 2e-16
## Expt.No.CPT:2007:Wall                -6.553920   2.787650  -2.351 0.019066
## Expt.No.CPT:2007:Watertown          -10.402501   2.787650  -3.732 0.000210
## Expt.No.CPT:2007:Winner             -12.813751   2.787650  -4.597 5.31e-06
## Expt.No.CPT:2007:Bison:Rep.No.2      -2.725000   2.787650  -0.978 0.328731
## Expt.No.CPT:2007:Brookings:Rep.No.2  -4.190833   2.787650  -1.503 0.133311
## Expt.No.CPT:2007:DLakesPea:Rep.No.2   0.459168   2.787650   0.165 0.869228
## Expt.No.CPT:2007:Hayes:Rep.No.2       1.408333   2.787650   0.505 0.613614
## Expt.No.CPT:2007:Kennebec:Rep.No.2    2.631682   2.787650   0.944 0.345551
## Expt.No.CPT:2007:Martin:Rep.No.2      1.308333   2.787650   0.469 0.639015
## Expt.No.CPT:2007:Onida:Rep.No.2       2.271667   2.787650   0.815 0.415474
## Expt.No.CPT:2007:Platte:Rep.No.2     -1.957917   2.787650  -0.702 0.482750
## Expt.No.CPT:2007:Selby:Rep.No.2       2.442500   2.787650   0.876 0.381305
## Expt.No.CPT:2007:Sturgis:Rep.No.2     1.550000   2.787650   0.556 0.578417
## Expt.No.CPT:2007:Wall:Rep.No.2        8.108943   2.787650   2.909 0.003771
## Expt.No.CPT:2007:Watertown:Rep.No.2   6.613333   2.787650   2.372 0.018011
## Expt.No.CPT:2007:Winner:Rep.No.2      1.185834   2.787650   0.425 0.670717
## Expt.No.CPT:2007:Bison:Rep.No.3      -1.825001   2.787650  -0.655 0.512947
## Expt.No.CPT:2007:Brookings:Rep.No.3  -3.180000   2.787650  -1.141 0.254463
## Expt.No.CPT:2007:DLakesPea:Rep.No.3  -3.361666   2.787650  -1.206 0.228360
## Expt.No.CPT:2007:Hayes:Rep.No.3       4.975000   2.787650   1.785 0.074858
## Expt.No.CPT:2007:Kennebec:Rep.No.3    0.902049   2.787650   0.324 0.746371
## Expt.No.CPT:2007:Martin:Rep.No.3      3.433334   2.787650   1.232 0.218607
## Expt.No.CPT:2007:Onida:Rep.No.3       0.751668   2.787650   0.270 0.787535
## Expt.No.CPT:2007:Platte:Rep.No.3     -4.097500   2.787650  -1.470 0.142157
## Expt.No.CPT:2007:Selby:Rep.No.3       0.151667   2.787650   0.054 0.956631
## Expt.No.CPT:2007:Sturgis:Rep.No.3    -1.225000   2.787650  -0.439 0.660514
## Expt.No.CPT:2007:Wall:Rep.No.3       -7.933019   2.787650  -2.846 0.004593
## Expt.No.CPT:2007:Watertown:Rep.No.3   3.924584   2.787650   1.408 0.159731
## Expt.No.CPT:2007:Winner:Rep.No.3      2.340417   2.787650   0.840 0.401510
## Expt.No.CPT:2007:Bison:Rep.No.4      -1.408334   2.787650  -0.505 0.613614
## Expt.No.CPT:2007:Brookings:Rep.No.4  -1.280833   2.787650  -0.459 0.646077
## Expt.No.CPT:2007:DLakesPea:Rep.No.4  -3.832916   2.787650  -1.375 0.169693
## Expt.No.CPT:2007:Hayes:Rep.No.4      11.216667   2.787650   4.024 6.52e-05
## Expt.No.CPT:2007:Kennebec:Rep.No.4    0.614842   2.787650   0.221 0.825516
## Expt.No.CPT:2007:Martin:Rep.No.4      1.516667   2.787650   0.544 0.586612
## Expt.No.CPT:2007:Onida:Rep.No.4       2.800834   2.787650   1.005 0.315461
## Expt.No.CPT:2007:Platte:Rep.No.4     -0.881666   2.787650  -0.316 0.751911
## Expt.No.CPT:2007:Selby:Rep.No.4       4.119167   2.787650   1.478 0.140064
## Expt.No.CPT:2007:Sturgis:Rep.No.4    -1.108333   2.787650  -0.398 0.691086
## Expt.No.CPT:2007:Wall:Rep.No.4       -6.132848   2.787650  -2.200 0.028214
## Expt.No.CPT:2007:Watertown:Rep.No.4   6.124167   2.787650   2.197 0.028437
## Expt.No.CPT:2007:Winner:Rep.No.4      1.903751   2.787650   0.683 0.494938
## eCriteria.Entry.No.:eExpt.No.         0.030572   0.006142   4.978 8.58e-07
##                                        
## (Intercept)                         ***
## Criteria.Entry.No.02                   
## Criteria.Entry.No.03                ***
## Criteria.Entry.No.04                ***
## Criteria.Entry.No.05                ** 
## Criteria.Entry.No.06                ***
## Criteria.Entry.No.07                ** 
## Criteria.Entry.No.08                ***
## Criteria.Entry.No.09                *  
## Criteria.Entry.No.10                   
## Criteria.Entry.No.11                   
## Criteria.Entry.No.12                   
## Expt.No.CPT:2007:Brookings          ** 
## Expt.No.CPT:2007:DLakesPea             
## Expt.No.CPT:2007:Hayes              *  
## Expt.No.CPT:2007:Kennebec           ***
## Expt.No.CPT:2007:Martin             ***
## Expt.No.CPT:2007:Onida                 
## Expt.No.CPT:2007:Platte             .  
## Expt.No.CPT:2007:Selby                 
## Expt.No.CPT:2007:Sturgis            ***
## Expt.No.CPT:2007:Wall               *  
## Expt.No.CPT:2007:Watertown          ***
## Expt.No.CPT:2007:Winner             ***
## Expt.No.CPT:2007:Bison:Rep.No.2        
## Expt.No.CPT:2007:Brookings:Rep.No.2    
## Expt.No.CPT:2007:DLakesPea:Rep.No.2    
## Expt.No.CPT:2007:Hayes:Rep.No.2        
## Expt.No.CPT:2007:Kennebec:Rep.No.2     
## Expt.No.CPT:2007:Martin:Rep.No.2       
## Expt.No.CPT:2007:Onida:Rep.No.2        
## Expt.No.CPT:2007:Platte:Rep.No.2       
## Expt.No.CPT:2007:Selby:Rep.No.2        
## Expt.No.CPT:2007:Sturgis:Rep.No.2      
## Expt.No.CPT:2007:Wall:Rep.No.2      ** 
## Expt.No.CPT:2007:Watertown:Rep.No.2 *  
## Expt.No.CPT:2007:Winner:Rep.No.2       
## Expt.No.CPT:2007:Bison:Rep.No.3        
## Expt.No.CPT:2007:Brookings:Rep.No.3    
## Expt.No.CPT:2007:DLakesPea:Rep.No.3    
## Expt.No.CPT:2007:Hayes:Rep.No.3     .  
## Expt.No.CPT:2007:Kennebec:Rep.No.3     
## Expt.No.CPT:2007:Martin:Rep.No.3       
## Expt.No.CPT:2007:Onida:Rep.No.3        
## Expt.No.CPT:2007:Platte:Rep.No.3       
## Expt.No.CPT:2007:Selby:Rep.No.3        
## Expt.No.CPT:2007:Sturgis:Rep.No.3      
## Expt.No.CPT:2007:Wall:Rep.No.3      ** 
## Expt.No.CPT:2007:Watertown:Rep.No.3    
## Expt.No.CPT:2007:Winner:Rep.No.3       
## Expt.No.CPT:2007:Bison:Rep.No.4        
## Expt.No.CPT:2007:Brookings:Rep.No.4    
## Expt.No.CPT:2007:DLakesPea:Rep.No.4    
## Expt.No.CPT:2007:Hayes:Rep.No.4     ***
## Expt.No.CPT:2007:Kennebec:Rep.No.4     
## Expt.No.CPT:2007:Martin:Rep.No.4       
## Expt.No.CPT:2007:Onida:Rep.No.4        
## Expt.No.CPT:2007:Platte:Rep.No.4       
## Expt.No.CPT:2007:Selby:Rep.No.4        
## Expt.No.CPT:2007:Sturgis:Rep.No.4      
## Expt.No.CPT:2007:Wall:Rep.No.4      *  
## Expt.No.CPT:2007:Watertown:Rep.No.4 *  
## Expt.No.CPT:2007:Winner:Rep.No.4       
## eCriteria.Entry.No.:eExpt.No.       ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.828 on 560 degrees of freedom
## Multiple R-squared:  0.7049, Adjusted R-squared:  0.6717 
## F-statistic: 21.23 on 63 and 560 DF,  p-value: < 2.2e-16
## 
##                                Df Sum Sq Mean Sq F value   Pr(>F)    
## Criteria.Entry.No.             11  17509    1592  34.137  < 2e-16 ***
## Expt.No.                       12  39698    3308  70.951  < 2e-16 ***
## Expt.No.:Rep.No.               39   3998     103   2.199 6.14e-05 ***
## eCriteria.Entry.No.:eExpt.No.   1   1155    1155  24.777 8.58e-07 ***
## Residuals                     560  26111      47                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -22.1268  -3.6831  -0.0066   3.7652  20.5194 
## 
## Coefficients: (1 not defined because of singularities)
##                                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)                          57.84208    2.29014  25.257  < 2e-16
## Criteria.Entry.No.02                 -2.07131    1.32439  -1.564 0.118398
## Criteria.Entry.No.03                 -5.22329    1.32439  -3.944 9.05e-05
## Criteria.Entry.No.04                -15.17534    1.32439 -11.458  < 2e-16
## Criteria.Entry.No.05                  3.88492    1.32439   2.933 0.003493
## Criteria.Entry.No.06                  6.35209    1.32439   4.796 2.08e-06
## Criteria.Entry.No.07                 -4.27983    1.32439  -3.232 0.001305
## Criteria.Entry.No.08                 -7.26744    1.32439  -5.487 6.23e-08
## Criteria.Entry.No.09                 -2.75782    1.32439  -2.082 0.037773
## Criteria.Entry.No.10                  0.58502    1.32439   0.442 0.658861
## Criteria.Entry.No.11                 -0.76620    1.32439  -0.579 0.563143
## Criteria.Entry.No.12                 -0.18131    1.32439  -0.137 0.891162
## Expt.No.CPT:2007:Brookings           -7.65428    2.86302  -2.674 0.007729
## Expt.No.CPT:2007:DLakesPea            2.99322    2.77342   1.079 0.280948
## Expt.No.CPT:2007:Hayes               -7.49296    2.85867  -2.621 0.009006
## Expt.No.CPT:2007:Kennebec           -11.94030    3.00857  -3.969 8.18e-05
## Expt.No.CPT:2007:Martin             -23.02542    3.60497  -6.387 3.60e-10
## Expt.No.CPT:2007:Onida               -1.14686    2.75936  -0.416 0.677845
## Expt.No.CPT:2007:Platte               5.02998    2.80324   1.794 0.073307
## Expt.No.CPT:2007:Selby               -2.67455    2.77010  -0.966 0.334716
## Expt.No.CPT:2007:Sturgis            -25.73170    3.78662  -6.795 2.82e-11
## Expt.No.CPT:2007:Wall                -6.93293    2.84426  -2.438 0.015104
## Expt.No.CPT:2007:Watertown          -11.00407    2.97202  -3.703 0.000235
## Expt.No.CPT:2007:Winner             -13.55476    3.07739  -4.405 1.27e-05
## Expt.No.CPT:2007:Bison:Rep.No.2      -2.72500    2.75693  -0.988 0.323383
## Expt.No.CPT:2007:Brookings:Rep.No.2  -4.19083    2.75693  -1.520 0.129058
## Expt.No.CPT:2007:DLakesPea:Rep.No.2   0.45917    2.75693   0.167 0.867785
## Expt.No.CPT:2007:Hayes:Rep.No.2       1.40833    2.75693   0.511 0.609672
## Expt.No.CPT:2007:Kennebec:Rep.No.2    2.63168    2.75693   0.955 0.340215
## Expt.No.CPT:2007:Martin:Rep.No.2      1.30833    2.75693   0.475 0.635288
## Expt.No.CPT:2007:Onida:Rep.No.2       2.27167    2.75693   0.824 0.410306
## Expt.No.CPT:2007:Platte:Rep.No.2     -1.95792    2.75693  -0.710 0.477893
## Expt.No.CPT:2007:Selby:Rep.No.2       2.44250    2.75693   0.886 0.376032
## Expt.No.CPT:2007:Sturgis:Rep.No.2     1.55000    2.75693   0.562 0.574196
## Expt.No.CPT:2007:Wall:Rep.No.2        8.10894    2.75693   2.941 0.003406
## Expt.No.CPT:2007:Watertown:Rep.No.2   6.61333    2.75693   2.399 0.016781
## Expt.No.CPT:2007:Winner:Rep.No.2      1.18583    2.75693   0.430 0.667271
## Expt.No.CPT:2007:Bison:Rep.No.3      -1.82500    2.75693  -0.662 0.508269
## Expt.No.CPT:2007:Brookings:Rep.No.3  -3.18000    2.75693  -1.153 0.249224
## Expt.No.CPT:2007:DLakesPea:Rep.No.3  -3.36167    2.75693  -1.219 0.223234
## Expt.No.CPT:2007:Hayes:Rep.No.3       4.97500    2.75693   1.805 0.071693
## Expt.No.CPT:2007:Kennebec:Rep.No.3    0.90205    2.75693   0.327 0.743646
## Expt.No.CPT:2007:Martin:Rep.No.3      3.43333    2.75693   1.245 0.213535
## Expt.No.CPT:2007:Onida:Rep.No.3       0.75167    2.75693   0.273 0.785227
## Expt.No.CPT:2007:Platte:Rep.No.3     -4.09750    2.75693  -1.486 0.137785
## Expt.No.CPT:2007:Selby:Rep.No.3       0.15167    2.75693   0.055 0.956148
## Expt.No.CPT:2007:Sturgis:Rep.No.3    -1.22500    2.75693  -0.444 0.656975
## Expt.No.CPT:2007:Wall:Rep.No.3       -7.93302    2.75693  -2.877 0.004164
## Expt.No.CPT:2007:Watertown:Rep.No.3   3.92458    2.75693   1.424 0.155148
## Expt.No.CPT:2007:Winner:Rep.No.3      2.34042    2.75693   0.849 0.396294
## Expt.No.CPT:2007:Bison:Rep.No.4      -1.40833    2.75693  -0.511 0.609672
## Expt.No.CPT:2007:Brookings:Rep.No.4  -1.28083    2.75693  -0.465 0.642411
## Expt.No.CPT:2007:DLakesPea:Rep.No.4  -3.83292    2.75693  -1.390 0.165005
## Expt.No.CPT:2007:Hayes:Rep.No.4      11.21667    2.75693   4.069 5.42e-05
## Expt.No.CPT:2007:Kennebec:Rep.No.4    0.61484    2.75693   0.223 0.823605
## Expt.No.CPT:2007:Martin:Rep.No.4      1.51667    2.75693   0.550 0.582454
## Expt.No.CPT:2007:Onida:Rep.No.4       2.80083    2.75693   1.016 0.310112
## Expt.No.CPT:2007:Platte:Rep.No.4     -0.88167    2.75693  -0.320 0.749242
## Expt.No.CPT:2007:Selby:Rep.No.4       4.11917    2.75693   1.494 0.135719
## Expt.No.CPT:2007:Sturgis:Rep.No.4    -1.10833    2.75693  -0.402 0.687828
## Expt.No.CPT:2007:Wall:Rep.No.4       -6.13285    2.75693  -2.225 0.026518
## Expt.No.CPT:2007:Watertown:Rep.No.4   6.12417    2.75693   2.221 0.026732
## Expt.No.CPT:2007:Winner:Rep.No.4      1.90375    2.75693   0.691 0.490151
## Criteria.Entry.No.01:eExpt.No.        0.40283    0.15762   2.556 0.010866
## Criteria.Entry.No.02:eExpt.No.        0.06411    0.15762   0.407 0.684367
## Criteria.Entry.No.03:eExpt.No.       -0.37748    0.15762  -2.395 0.016962
## Criteria.Entry.No.04:eExpt.No.       -0.40666    0.15762  -2.580 0.010139
## Criteria.Entry.No.05:eExpt.No.        0.08910    0.15762   0.565 0.572138
## Criteria.Entry.No.06:eExpt.No.        0.15470    0.15762   0.981 0.326796
## Criteria.Entry.No.07:eExpt.No.        0.01611    0.15762   0.102 0.918631
## Criteria.Entry.No.08:eExpt.No.       -0.29461    0.15762  -1.869 0.062142
## Criteria.Entry.No.09:eExpt.No.       -0.15624    0.15762  -0.991 0.322021
## Criteria.Entry.No.10:eExpt.No.       -0.09992    0.15762  -0.634 0.526404
## Criteria.Entry.No.11:eExpt.No.       -0.08590    0.15762  -0.545 0.585995
## Criteria.Entry.No.12:eExpt.No.             NA         NA      NA       NA
##                                        
## (Intercept)                         ***
## Criteria.Entry.No.02                   
## Criteria.Entry.No.03                ***
## Criteria.Entry.No.04                ***
## Criteria.Entry.No.05                ** 
## Criteria.Entry.No.06                ***
## Criteria.Entry.No.07                ** 
## Criteria.Entry.No.08                ***
## Criteria.Entry.No.09                *  
## Criteria.Entry.No.10                   
## Criteria.Entry.No.11                   
## Criteria.Entry.No.12                   
## Expt.No.CPT:2007:Brookings          ** 
## Expt.No.CPT:2007:DLakesPea             
## Expt.No.CPT:2007:Hayes              ** 
## Expt.No.CPT:2007:Kennebec           ***
## Expt.No.CPT:2007:Martin             ***
## Expt.No.CPT:2007:Onida                 
## Expt.No.CPT:2007:Platte             .  
## Expt.No.CPT:2007:Selby                 
## Expt.No.CPT:2007:Sturgis            ***
## Expt.No.CPT:2007:Wall               *  
## Expt.No.CPT:2007:Watertown          ***
## Expt.No.CPT:2007:Winner             ***
## Expt.No.CPT:2007:Bison:Rep.No.2        
## Expt.No.CPT:2007:Brookings:Rep.No.2    
## Expt.No.CPT:2007:DLakesPea:Rep.No.2    
## Expt.No.CPT:2007:Hayes:Rep.No.2        
## Expt.No.CPT:2007:Kennebec:Rep.No.2     
## Expt.No.CPT:2007:Martin:Rep.No.2       
## Expt.No.CPT:2007:Onida:Rep.No.2        
## Expt.No.CPT:2007:Platte:Rep.No.2       
## Expt.No.CPT:2007:Selby:Rep.No.2        
## Expt.No.CPT:2007:Sturgis:Rep.No.2      
## Expt.No.CPT:2007:Wall:Rep.No.2      ** 
## Expt.No.CPT:2007:Watertown:Rep.No.2 *  
## Expt.No.CPT:2007:Winner:Rep.No.2       
## Expt.No.CPT:2007:Bison:Rep.No.3        
## Expt.No.CPT:2007:Brookings:Rep.No.3    
## Expt.No.CPT:2007:DLakesPea:Rep.No.3    
## Expt.No.CPT:2007:Hayes:Rep.No.3     .  
## Expt.No.CPT:2007:Kennebec:Rep.No.3     
## Expt.No.CPT:2007:Martin:Rep.No.3       
## Expt.No.CPT:2007:Onida:Rep.No.3        
## Expt.No.CPT:2007:Platte:Rep.No.3       
## Expt.No.CPT:2007:Selby:Rep.No.3        
## Expt.No.CPT:2007:Sturgis:Rep.No.3      
## Expt.No.CPT:2007:Wall:Rep.No.3      ** 
## Expt.No.CPT:2007:Watertown:Rep.No.3    
## Expt.No.CPT:2007:Winner:Rep.No.3       
## Expt.No.CPT:2007:Bison:Rep.No.4        
## Expt.No.CPT:2007:Brookings:Rep.No.4    
## Expt.No.CPT:2007:DLakesPea:Rep.No.4    
## Expt.No.CPT:2007:Hayes:Rep.No.4     ***
## Expt.No.CPT:2007:Kennebec:Rep.No.4     
## Expt.No.CPT:2007:Martin:Rep.No.4       
## Expt.No.CPT:2007:Onida:Rep.No.4        
## Expt.No.CPT:2007:Platte:Rep.No.4       
## Expt.No.CPT:2007:Selby:Rep.No.4        
## Expt.No.CPT:2007:Sturgis:Rep.No.4      
## Expt.No.CPT:2007:Wall:Rep.No.4      *  
## Expt.No.CPT:2007:Watertown:Rep.No.4 *  
## Expt.No.CPT:2007:Winner:Rep.No.4       
## Criteria.Entry.No.01:eExpt.No.      *  
## Criteria.Entry.No.02:eExpt.No.         
## Criteria.Entry.No.03:eExpt.No.      *  
## Criteria.Entry.No.04:eExpt.No.      *  
## Criteria.Entry.No.05:eExpt.No.         
## Criteria.Entry.No.06:eExpt.No.         
## Criteria.Entry.No.07:eExpt.No.         
## Criteria.Entry.No.08:eExpt.No.      .  
## Criteria.Entry.No.09:eExpt.No.         
## Criteria.Entry.No.10:eExpt.No.         
## Criteria.Entry.No.11:eExpt.No.         
## Criteria.Entry.No.12:eExpt.No.         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.753 on 550 degrees of freedom
## Multiple R-squared:  0.7165, Adjusted R-squared:  0.6789 
## F-statistic: 19.04 on 73 and 550 DF,  p-value: < 2.2e-16
## 
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -27.5936  -4.3246  -0.1103   3.8202  26.2489 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## Criteria.Entry.No.01            50.4397     1.0062  50.127  < 2e-16 ***
## Criteria.Entry.No.02            48.3684     1.0062  48.069  < 2e-16 ***
## Criteria.Entry.No.03            45.2164     1.0062  44.936  < 2e-16 ***
## Criteria.Entry.No.04            35.2643     1.0062  35.046  < 2e-16 ***
## Criteria.Entry.No.05            54.3246     1.0062  53.988  < 2e-16 ***
## Criteria.Entry.No.06            56.7918     1.0062  56.440  < 2e-16 ***
## Criteria.Entry.No.07            46.1599     1.0062  45.874  < 2e-16 ***
## Criteria.Entry.No.08            43.1722     1.0062  42.905  < 2e-16 ***
## Criteria.Entry.No.09            47.6819     1.0062  47.386  < 2e-16 ***
## Criteria.Entry.No.10            51.0247     1.0062  50.708  < 2e-16 ***
## Criteria.Entry.No.11            49.6735     1.0062  49.366  < 2e-16 ***
## Criteria.Entry.No.12            50.2584     1.0062  49.947  < 2e-16 ***
## Criteria.Entry.No.01:eExpt.No.   1.3794     0.1198  11.519  < 2e-16 ***
## Criteria.Entry.No.02:eExpt.No.   1.0407     0.1198   8.690  < 2e-16 ***
## Criteria.Entry.No.03:eExpt.No.   0.5991     0.1198   5.003 7.43e-07 ***
## Criteria.Entry.No.04:eExpt.No.   0.5700     0.1198   4.759 2.44e-06 ***
## Criteria.Entry.No.05:eExpt.No.   1.0657     0.1198   8.899  < 2e-16 ***
## Criteria.Entry.No.06:eExpt.No.   1.1313     0.1198   9.447  < 2e-16 ***
## Criteria.Entry.No.07:eExpt.No.   0.9927     0.1198   8.289 7.48e-16 ***
## Criteria.Entry.No.08:eExpt.No.   0.6820     0.1198   5.695 1.93e-08 ***
## Criteria.Entry.No.09:eExpt.No.   0.8204     0.1198   6.850 1.83e-11 ***
## Criteria.Entry.No.10:eExpt.No.   0.8767     0.1198   7.321 7.97e-13 ***
## Criteria.Entry.No.11:eExpt.No.   0.8907     0.1198   7.438 3.56e-13 ***
## Criteria.Entry.No.12:eExpt.No.   0.9766     0.1198   8.155 2.05e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.256 on 600 degrees of freedom
## Multiple R-squared:  0.9795, Adjusted R-squared:  0.9786 
## F-statistic:  1192 on 24 and 600 DF,  p-value: < 2.2e-16
## 
##                               Df  Sum Sq Mean Sq F value Pr(>F)    
## Criteria.Entry.No.            12 1467088  122257 2322.05 <2e-16 ***
## Criteria.Entry.No.:eExpt.No.  12   39372    3281   62.32 <2e-16 ***
## Residuals                    600   31590      53                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##                               Df Sum Sq Mean Sq F value   Pr(>F)    
## Criteria.Entry.No.            11  17509    1592  34.902  < 2e-16 ***
## Expt.No.                      12  39698    3308  72.541  < 2e-16 ***
## Expt.No.:Rep.No.              39   3998     103   2.248 3.82e-05 ***
## Criteria.Entry.No.:eExpt.No.  11   2184     199   4.353 2.88e-06 ***
## Residuals                    550  25082      46                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 9.901533
## [1] Interaction sd Value:
## [1] 6.172434
## [1] Error sd Value:
## [1] 4.125639
## [1] Pairs:
## [[1]]
## [1] 4 1
## 
## [[2]]
## [1] 1 8
## 
## [[3]]
## [1] 4 8
## 
## [[4]]
## [1] 1 9
## 
## [[5]]
## [1] 2 9
## 
## [[6]]
## [1] 3 9
## 
## [[7]]
## [1] 4 9
## 
## [[8]]
## [1] 6 9
## 
## [[9]]
## [1] 7 9
## 
## [[10]]
## [1]  4 10
## 
## [[11]]
## [1]  3 11
## 
## [[12]]
## [1]  4 11
## 
## [[13]]
## [1]  5 11
## 
## [[14]]
## [1]  5 12
## 
## [[15]]
## [1]  6 12
## 
## [[16]]
## [1]  7 12
## 
## [1] 
## [1]
gyb.res <- standard.sensitivity.plot(gy.dat,
                          response = response,
                          TreatmentName = TreatmentName,
                          TrialName = TrialName,
                          RepName=RepName,
                          outliers=2,
                          method="ave",
                          plot.outliers=TRUE,legend.columns=3)

gy2.res <- standard.sensitivity.plot(gy.dat,
                                     response = response,
                          TreatmentName = TreatmentName,
                          TrialName = TrialName,
                          RepName=RepName,
                          outliers=2.4,
                          dual.dendrogram=TRUE,
                          method="ave",
                          plot.outliers=TRUE,legend.columns=3)

means.matrix <- tapply(gy.dat$Plot.Mean,list(gy.dat$Criteria.Entry.No.,gy.dat$Expt.No.),mean)
decomp <- decompose.means.table(means.matrix)
txt.matrix <- decomp$gamma
mean(unlist(txt.matrix),na.rm=TRUE)
## [1] -2.095165e-15
max <- sd(unlist(txt.matrix),na.rm=TRUE)
norm.mat <- abs(txt.matrix)
crit <- 3*max
gy.means$means.table - means.matrix
##    CPT:2007:Bison CPT:2007:Brookings CPT:2007:DLakesPea CPT:2007:Hayes
## 01   5.258016e-13      -1.492140e-13       8.526513e-14  -1.776357e-13
## 02   4.618528e-13      -1.065814e-13       0.000000e+00  -2.131628e-13
## 03   3.055334e-13      -4.973799e-14       1.492140e-13  -1.492140e-13
## 04   6.679102e-13       5.329071e-14       7.105427e-15  -1.563194e-13
## 05  -3.410605e-13      -1.065814e-13      -7.105427e-15  -2.273737e-13
## 06   1.186606e-12      -1.421085e-13       5.684342e-14  -2.344791e-13
## 07   0.000000e+00      -1.421085e-14       0.000000e+00  -2.060574e-13
## 08   4.263256e-14      -5.684342e-14       7.105427e-14  -1.847411e-13
## 09   2.131628e-14      -2.842171e-14       8.526513e-14  -1.634248e-13
## 10   4.192202e-13      -9.237056e-14       4.263256e-14  -1.847411e-13
## 11   1.421085e-13      -8.526513e-14       1.136868e-13  -1.634248e-13
## 12   2.060574e-13      -1.065814e-13       3.552714e-14  -1.918465e-13
##    CPT:2007:Kennebec CPT:2007:Martin CPT:2007:Onida CPT:2007:Platte
## 01      1.705303e-13   -4.725109e-13   1.989520e-13    2.557954e-13
## 02      3.197442e-13   -4.689582e-13   1.989520e-13    2.629008e-13
## 03      3.836931e-13   -4.121148e-13   2.131628e-13    2.273737e-13
## 04      4.369838e-13   -3.907985e-13   2.913225e-13    2.486900e-13
## 05      3.197442e-13   -4.547474e-13   2.060574e-13    2.415845e-13
## 06      3.481659e-13   -4.263256e-13   2.202682e-13    2.415845e-13
## 07      3.836931e-13   -4.547474e-13   2.771117e-13    2.842171e-13
## 08      3.623768e-13   -4.760636e-13   1.918465e-13    2.984279e-13
## 09      4.121148e-13   -4.263256e-13   2.486900e-13    2.984279e-13
## 10      3.979039e-13   -4.547474e-13   2.344791e-13    2.344791e-13
## 11      3.979039e-13   -4.405365e-13   2.202682e-13    2.984279e-13
## 12      3.907985e-13   -4.689582e-13   2.415845e-13    2.984279e-13
##    CPT:2007:Selby CPT:2007:Sturgis CPT:2007:Wall CPT:2007:Watertown
## 01   2.415845e-13     4.263256e-14  1.776357e-13       1.492140e-13
## 02   1.705303e-13    -4.263256e-14  1.421085e-13       1.350031e-13
## 03   3.055334e-13    -2.842171e-14  1.563194e-13       1.847411e-13
## 04   2.167155e-13     1.065814e-14  2.202682e-13       2.273737e-13
## 05   1.847411e-13    -6.394885e-14  1.634248e-13       1.705303e-13
## 06   2.557954e-13    -4.973799e-14  1.918465e-13       1.705303e-13
## 07   2.486900e-13    -3.197442e-14  1.207923e-13       1.350031e-13
## 08   2.060574e-13    -2.486900e-14  1.563194e-13       1.563194e-13
## 09   2.415845e-13    -1.065814e-14  1.989520e-13       2.202682e-13
## 10   2.486900e-13    -2.131628e-14  1.705303e-13       1.918465e-13
## 11   2.273737e-13    -7.105427e-15  1.705303e-13       2.131628e-13
## 12   2.486900e-13    -1.421085e-14  1.918465e-13       1.989520e-13
##    CPT:2007:Winner
## 01    1.421085e-13
## 02    1.492140e-13
## 03    1.563194e-13
## 04    2.060574e-13
## 05    1.421085e-13
## 06    1.705303e-13
## 07    1.421085e-13
## 08    1.705303e-13
## 09    1.847411e-13
## 10    1.634248e-13
## 11    1.918465e-13
## 12    2.060574e-13
gy.res$cluster$score/gy.res$add.cluster$score
##  [1]  1.3927179  1.2256504  1.0296006  1.2545966  0.9612729  0.9204759
##  [7]  0.8777299  0.2101115  0.4671611 77.5177481  0.9335306  0.9547280
gy.res$cluster$clusters
##  [1] 1 1 2 1 1 3 2 2 2 3 1 1 1
gy.res$add.cluster$clusters
##  1  2  3  4  5  6  7  8  9 10 11 12 13 
##  1  2  1  1  2  3  1  1  1  3  2  2  2
gy.res$cluster$means.hc$height
##  [1]  12.75757  16.01341  19.38159  19.76017  23.51187  24.68833  26.87330
##  [8]  34.53185  34.78175  47.55139  62.18434 103.29494
gy.res$add.cluster$means.hc$height
##  [1]  2.215582  2.680709  2.950214  6.256760  6.350852  7.127390  9.175900
##  [8] 14.960588 18.080083 19.759451 50.149726 95.406771
gy.res$cluster$means.hc$height/gy.res$add.cluster$means.hc$height
##  [1] 5.758115 5.973574 6.569555 3.158211 3.702160 3.463867 2.928683
##  [8] 2.308188 1.923760 2.406514 1.239974 1.082679
gy.res$cluster$means.hc$order
##  [1]  6 10  9  8  3  7 11  1  4 12  5  2 13
gy.res$add.cluster$means.hc$order
##  [1]  6 10 11 12 13  2  5  4  1  9  8  3  7
gy.res$cluster$means.hc$merge
##       [,1] [,2]
##  [1,]   -2  -13
##  [2,]   -1   -4
##  [3,]   -3   -7
##  [4,]   -5    1
##  [5,]   -6  -10
##  [6,]   -8    3
##  [7,]  -11    2
##  [8,]   -9    6
##  [9,]  -12    4
## [10,]    7    9
## [11,]    8   10
## [12,]    5   11
gy.res$add.cluster$means.hc$merge
##       [,1] [,2]
##  [1,]   -1   -9
##  [2,]   -3   -7
##  [3,]   -2   -5
##  [4,]  -11  -12
##  [5,]   -4    1
##  [6,]  -13    3
##  [7,]   -8    2
##  [8,]   -6  -10
##  [9,]    4    6
## [10,]    5    7
## [11,]    9   10
## [12,]    8   11
gy.res$cluster$means.hc$merge==gy.res$add.cluster$means.hc$merge
##        [,1]  [,2]
##  [1,] FALSE FALSE
##  [2,] FALSE FALSE
##  [3,] FALSE FALSE
##  [4,] FALSE FALSE
##  [5,] FALSE FALSE
##  [6,] FALSE  TRUE
##  [7,] FALSE  TRUE
##  [8,] FALSE FALSE
##  [9,] FALSE FALSE
## [10,] FALSE FALSE
## [11,] FALSE  TRUE
## [12,] FALSE  TRUE
tdf.tbl <- anova(gy.res$tdf$multiplicative.lm)
aov.tbl <- gy.res$aov
tdf.tbl
## Analysis of Variance Table
## 
## Response: Plot.Mean
##                                Df Sum Sq Mean Sq F value    Pr(>F)    
## Criteria.Entry.No.             11  17509  1591.7 34.1373 < 2.2e-16 ***
## Expt.No.                       12  39698  3308.2 70.9513 < 2.2e-16 ***
## Expt.No.:Rep.No.               39   3998   102.5  2.1988 6.139e-05 ***
## eCriteria.Entry.No.:eExpt.No.   1   1155  1155.3 24.7774 8.579e-07 ***
## Residuals                     560  26111    46.6                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aov.tbl
## Analysis of Variance Table
## 
## Response: Plot.Mean
##                              Df Sum Sq Mean Sq  F value    Pr(>F)    
## Expt.No.                     12  39698  3308.2 194.3594 < 2.2e-16 ***
## Criteria.Entry.No.           11  17509  1591.7  93.5136 < 2.2e-16 ***
## Expt.No.:Criteria.Entry.No. 132  19964   151.2   8.8856 < 2.2e-16 ***
## Expt.No.:Rep.No.             39   3998   102.5   6.0232 < 2.2e-16 ***
## Residuals                   429   7302    17.0                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
recompute.tdf.aov(tdf.tbl,aov.tbl)
## Analysis of Variance Table
## 
## Response: Plot.Mean
##                                Df Sum Sq Mean Sq F value    Pr(>F)    
## Criteria.Entry.No.             11  17509  1591.7 34.1373 < 2.2e-16 ***
## Expt.No.                       12  39698  3308.2 70.9513 < 2.2e-16 ***
## Expt.No.:Rep.No.               39   3998   102.5  2.1988 6.139e-05 ***
## eCriteria.Entry.No.:eExpt.No.   1   1155  1155.3 15.4416 0.0003478 ***
## Residuals                      38   2843    74.8  4.3955 1.044e-14 ***
## Residuals1                    429   7302    17.0                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

last = dim(aov.tbl)[1] last.row <- aov.tbl[last,] tdf.last <- dim(tdf.tbl)[1] colnames(last.row) <- colnames(tdf.tbl) tdf.tbl <- rbind(tdf.tbl,last.row) #compute interaction residuals by subtracting 1df row from txt row tdf.tbl[tdf.last,] <- aov.tbl[last-1,] - tdf.tbl[tdf.last-1,] #recompute residuals tdf.tbl[tdf.last,3] <- tdf.tbl[tdf.last,2]/tdf.tbl[tdf.last,1] #test treatment:trial against interaction residual tdf.tbl[tdf.last-1,4] <- tdf.tbl[tdf.last-1,3]/tdf.tbl[tdf.last,3] tdf.tbl[tdf.last-1,5] <- 1-pf(tdf.tbl[tdf.last-1,4],tdf.tbl[tdf.last-1,1],tdf.tbl[tdf.last,1])

#test interaction residual against experimental residual tdf.tbl[tdf.last,4] <- tdf.tbl[tdf.last,3]/last.row[3] tdf.tbl[tdf.last,5] <- 1-pf(tdf.tbl[tdf.last,4],tdf.tbl[tdf.last,1],as.numeric(last.row[1]))

return(tdf.tbl)

```

tw.res <- standard.sensitivity.plot(tw.dat,
                                     response = response,
                          TreatmentName = TreatmentName,
                          TrialName = TrialName,
                          RepName=RepName,
                          dual.dendrogram=TRUE,
                          plot.outliers=TRUE,legend.columns=3)

print.stdplot(tw.res)
## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
## 
## Response: Plot.Mean
##                              Df Sum Sq Mean Sq  F value    Pr(>F)    
## Expt.No.                     12 4128.3  344.02 168.3919 < 2.2e-16 ***
## Criteria.Entry.No.           11 1880.3  170.93  83.6688 < 2.2e-16 ***
## Expt.No.:Criteria.Entry.No. 132 1631.9   12.36   6.0515 < 2.2e-16 ***
## Expt.No.:Rep.No.             39  301.6    7.73   3.7854 4.862e-12 ***
## Residuals                   425  868.3    2.04                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula: 
## Plot.Mean ~ Expt.No. + (1 | Expt.No./Rep.No.) + (1 | Expt.No.:Criteria.Entry.No.)
##    Data: plot.dat
## 
## REML criterion at convergence: 2613.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4708 -0.4532  0.0575  0.5014  2.6909 
## 
## Random effects:
##  Groups                      Name        Variance Std.Dev.
##  Expt.No.:Criteria.Entry.No. (Intercept) 5.6585   2.3788  
##  Rep.No.:Expt.No.            (Intercept) 0.4781   0.6914  
##  Expt.No.                    (Intercept) 7.4666   2.7325  
##  Residual                                2.0446   1.4299  
## Number of obs: 620, groups:  
## Expt.No.:Criteria.Entry.No., 156; Rep.No.:Expt.No., 52; Expt.No., 13
## 
## Fixed effects:
##                            Estimate Std. Error t value
## (Intercept)                  63.073      2.846  22.161
## Expt.No.CPT:2007:Brookings   -5.383      4.026  -1.337
## Expt.No.CPT:2007:DLakesPea   -5.985      4.025  -1.487
## Expt.No.CPT:2007:Hayes       -1.221      4.025  -0.303
## Expt.No.CPT:2007:Kennebec    -9.357      4.025  -2.325
## Expt.No.CPT:2007:Martin      -3.223      4.025  -0.801
## Expt.No.CPT:2007:Onida       -5.841      4.025  -1.451
## Expt.No.CPT:2007:Platte      -7.438      4.025  -1.848
## Expt.No.CPT:2007:Selby       -7.150      4.025  -1.776
## Expt.No.CPT:2007:Sturgis     -1.927      4.025  -0.479
## Expt.No.CPT:2007:Wall        -4.103      4.025  -1.019
## Expt.No.CPT:2007:Watertown   -3.102      4.025  -0.771
## Expt.No.CPT:2007:Winner      -5.661      4.025  -1.407
## 
## Correlation of Fixed Effects:
##                    (Intr) E.N.CPT:2007:B E.N.CPT:2007:D E.N.CPT:2007:H
## E.N.CPT:2007:B     -0.707                                             
## E.N.CPT:2007:D     -0.707  0.500                                      
## E.N.CPT:2007:H     -0.707  0.500          0.500                       
## E.N.CPT:2007:K     -0.707  0.500          0.500          0.500        
## E.N.CPT:2007:M     -0.707  0.500          0.500          0.500        
## E.N.CPT:2007:O     -0.707  0.500          0.500          0.500        
## E.N.CPT:2007:P     -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:Sl -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:St -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wl -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wt -0.707  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wn -0.707  0.500          0.500          0.500        
##                    E.N.CPT:2007:K E.N.CPT:2007:M E.N.CPT:2007:O
## E.N.CPT:2007:B                                                 
## E.N.CPT:2007:D                                                 
## E.N.CPT:2007:H                                                 
## E.N.CPT:2007:K                                                 
## E.N.CPT:2007:M      0.500                                      
## E.N.CPT:2007:O      0.500          0.500                       
## E.N.CPT:2007:P      0.500          0.500          0.500        
## Expt.N.CPT:2007:Sl  0.500          0.500          0.500        
## Expt.N.CPT:2007:St  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wl  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wt  0.500          0.500          0.500        
## Expt.N.CPT:2007:Wn  0.500          0.500          0.500        
##                    E.N.CPT:2007:P Expt.N.CPT:2007:Sl Expt.N.CPT:2007:St
## E.N.CPT:2007:B                                                         
## E.N.CPT:2007:D                                                         
## E.N.CPT:2007:H                                                         
## E.N.CPT:2007:K                                                         
## E.N.CPT:2007:M                                                         
## E.N.CPT:2007:O                                                         
## E.N.CPT:2007:P                                                         
## Expt.N.CPT:2007:Sl  0.500                                              
## Expt.N.CPT:2007:St  0.500          0.500                               
## Expt.N.CPT:2007:Wl  0.500          0.500              0.500            
## Expt.N.CPT:2007:Wt  0.500          0.500              0.500            
## Expt.N.CPT:2007:Wn  0.500          0.500              0.500            
##                    Expt.N.CPT:2007:Wl Expt.N.CPT:2007:Wt
## E.N.CPT:2007:B                                          
## E.N.CPT:2007:D                                          
## E.N.CPT:2007:H                                          
## E.N.CPT:2007:K                                          
## E.N.CPT:2007:M                                          
## E.N.CPT:2007:O                                          
## E.N.CPT:2007:P                                          
## Expt.N.CPT:2007:Sl                                      
## Expt.N.CPT:2007:St                                      
## Expt.N.CPT:2007:Wl                                      
## Expt.N.CPT:2007:Wt  0.500                               
## Expt.N.CPT:2007:Wn  0.500              0.500            
## [1] 
## [1] Stability
## [1] ----------------------------------------------------
##    Treatment     Slope   Intercept     Mean       SD            b
## 1          1 1.0236239  -0.6044865 59.19784 3.206041  0.023623936
## 2          2 0.5756150  25.4506800 59.07935 1.786156 -0.424385033
## 3          3 1.4578990 -27.7130516 57.46056 4.173253  0.457899021
## 4          4 1.6812709 -41.1163899 57.10709 4.908317  0.681270932
## 5          5 0.5923172  26.2070972 60.81155 2.060720 -0.407682801
## 6          6 0.5074963  30.9999788 60.64901 2.078896 -0.492503662
## 7          7 1.0076033   0.4871182 59.35348 3.057007  0.007603260
## 8          8 1.3668135 -24.4649125 55.38728 3.797264  0.366813461
## 9          9 0.5600023  28.0259437 60.74249 1.988947 -0.439997720
## 10        10 1.0024725  -1.3033282 57.26328 2.898771  0.002472472
## 11        11 0.8397078   8.7686355 57.82618 2.473374 -0.160292179
## 12        12 1.3851783 -24.7372847 56.18782 3.860661  0.385178314
##             Pb          bR2
## 1  0.900928919 1.473171e-03
## 2  0.001438911 6.180498e-01
## 3  0.016789197 4.188968e-01
## 4  0.009307985 4.736197e-01
## 5  0.018238667 4.108484e-01
## 6  0.017495975 4.149003e-01
## 7  0.962931685 2.054539e-04
## 8  0.006547976 5.040948e-01
## 9  0.011892635 4.514403e-01
## 10 0.984054800 3.799709e-05
## 11 0.188308310 1.517212e-01
## 12 0.006984747 4.986186e-01
## [1] 
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = as.formula(modelString), data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.8885 -1.1662  0.0518  1.2642  5.2734 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)                         63.77828    0.63309 100.741  < 2e-16
## Criteria.Entry.No.02                -0.11849    0.39073  -0.303 0.761821
## Criteria.Entry.No.03                -1.73728    0.39073  -4.446 1.06e-05
## Criteria.Entry.No.04                -1.99783    0.39720  -5.030 6.64e-07
## Criteria.Entry.No.05                 1.61371    0.39073   4.130 4.19e-05
## Criteria.Entry.No.06                 1.45118    0.39073   3.714 0.000225
## Criteria.Entry.No.07                 0.15564    0.39073   0.398 0.690536
## Criteria.Entry.No.08                -3.81055    0.39073  -9.752  < 2e-16
## Criteria.Entry.No.09                 1.54465    0.39073   3.953 8.71e-05
## Criteria.Entry.No.10                -1.93456    0.39073  -4.951 9.80e-07
## Criteria.Entry.No.11                -1.39586    0.39282  -3.553 0.000413
## Criteria.Entry.No.12                -3.01001    0.39073  -7.704 6.11e-14
## Expt.No.CPT:2007:Brookings          -3.61917    0.81337  -4.450 1.04e-05
## Expt.No.CPT:2007:DLakesPea          -5.21625    0.81337  -6.413 3.05e-10
## Expt.No.CPT:2007:Hayes              -1.45000    0.81337  -1.783 0.075181
## Expt.No.CPT:2007:Kennebec           -7.98293    0.83202  -9.595  < 2e-16
## Expt.No.CPT:2007:Martin             -3.15000    0.81337  -3.873 0.000120
## Expt.No.CPT:2007:Onida              -5.79750    0.81337  -7.128 3.18e-12
## Expt.No.CPT:2007:Platte             -6.18250    0.81337  -7.601 1.26e-13
## Expt.No.CPT:2007:Selby              -7.44708    0.81337  -9.156  < 2e-16
## Expt.No.CPT:2007:Sturgis            -2.25833    0.81337  -2.777 0.005680
## Expt.No.CPT:2007:Wall               -4.07268    0.81337  -5.007 7.43e-07
## Expt.No.CPT:2007:Watertown          -1.92708    0.81337  -2.369 0.018165
## Expt.No.CPT:2007:Winner             -5.66875    0.81337  -6.969 9.04e-12
## Expt.No.CPT:2007:Bison:Rep.No.2     -0.85833    0.81337  -1.055 0.291757
## Expt.No.CPT:2007:Brookings:Rep.No.2 -1.71124    0.83202  -2.057 0.040179
## Expt.No.CPT:2007:DLakesPea:Rep.No.2 -0.03417    0.81337  -0.042 0.966509
## Expt.No.CPT:2007:Hayes:Rep.No.2      0.03333    0.81337   0.041 0.967325
## Expt.No.CPT:2007:Kennebec:Rep.No.2  -3.53691    0.83202  -4.251 2.50e-05
## Expt.No.CPT:2007:Martin:Rep.No.2     0.72500    0.81337   0.891 0.373127
## Expt.No.CPT:2007:Onida:Rep.No.2      0.11000    0.81337   0.135 0.892472
## Expt.No.CPT:2007:Platte:Rep.No.2    -1.34542    0.81337  -1.654 0.098668
## Expt.No.CPT:2007:Selby:Rep.No.2      0.65708    0.81337   0.808 0.419522
## Expt.No.CPT:2007:Sturgis:Rep.No.2    1.17500    0.81337   1.445 0.149133
## Expt.No.CPT:2007:Wall:Rep.No.2       0.49103    0.81337   0.604 0.546290
## Expt.No.CPT:2007:Watertown:Rep.No.2 -1.62250    0.81337  -1.995 0.046554
## Expt.No.CPT:2007:Winner:Rep.No.2     0.11583    0.81337   0.142 0.886807
## Expt.No.CPT:2007:Bison:Rep.No.3     -0.27500    0.81337  -0.338 0.735416
## Expt.No.CPT:2007:Brookings:Rep.No.3 -2.69670    0.83202  -3.241 0.001262
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 -1.42500    0.81337  -1.752 0.080331
## Expt.No.CPT:2007:Hayes:Rep.No.3      0.85000    0.81337   1.045 0.296462
## Expt.No.CPT:2007:Kennebec:Rep.No.3  -1.53941    0.83202  -1.850 0.064814
## Expt.No.CPT:2007:Martin:Rep.No.3     0.10833    0.81337   0.133 0.894091
## Expt.No.CPT:2007:Onida:Rep.No.3     -0.59667    0.81337  -0.734 0.463519
## Expt.No.CPT:2007:Platte:Rep.No.3    -1.85417    0.81337  -2.280 0.023009
## Expt.No.CPT:2007:Selby:Rep.No.3     -0.13292    0.81337  -0.163 0.870253
## Expt.No.CPT:2007:Sturgis:Rep.No.3    0.39167    0.81337   0.482 0.630326
## Expt.No.CPT:2007:Wall:Rep.No.3      -0.31208    0.81337  -0.384 0.701358
## Expt.No.CPT:2007:Watertown:Rep.No.3 -1.45333    0.81337  -1.787 0.074514
## Expt.No.CPT:2007:Winner:Rep.No.3    -0.06000    0.81337  -0.074 0.941223
## Expt.No.CPT:2007:Bison:Rep.No.4      1.39167    0.81337   1.711 0.087642
## Expt.No.CPT:2007:Brookings:Rep.No.4 -2.30397    0.83202  -2.769 0.005808
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 -1.35875    0.81337  -1.671 0.095381
## Expt.No.CPT:2007:Hayes:Rep.No.4      0.29167    0.81337   0.359 0.720039
## Expt.No.CPT:2007:Kennebec:Rep.No.4  -0.22341    0.83202  -0.269 0.788404
## Expt.No.CPT:2007:Martin:Rep.No.4    -0.86667    0.81337  -1.066 0.287102
## Expt.No.CPT:2007:Onida:Rep.No.4      0.57250    0.81337   0.704 0.481815
## Expt.No.CPT:2007:Platte:Rep.No.4    -1.56542    0.81337  -1.925 0.054789
## Expt.No.CPT:2007:Selby:Rep.No.4      0.92250    0.81337   1.134 0.257213
## Expt.No.CPT:2007:Sturgis:Rep.No.4    0.01667    0.81337   0.020 0.983658
## Expt.No.CPT:2007:Wall:Rep.No.4      -0.04230    0.81337  -0.052 0.958543
## Expt.No.CPT:2007:Watertown:Rep.No.4 -1.36417    0.81337  -1.677 0.094070
## Expt.No.CPT:2007:Winner:Rep.No.4     0.23208    0.81337   0.285 0.775494
## eCriteria.Entry.No.:eExpt.No.       -0.17107    0.01979  -8.645  < 2e-16
##                                        
## (Intercept)                         ***
## Criteria.Entry.No.02                   
## Criteria.Entry.No.03                ***
## Criteria.Entry.No.04                ***
## Criteria.Entry.No.05                ***
## Criteria.Entry.No.06                ***
## Criteria.Entry.No.07                   
## Criteria.Entry.No.08                ***
## Criteria.Entry.No.09                ***
## Criteria.Entry.No.10                ***
## Criteria.Entry.No.11                ***
## Criteria.Entry.No.12                ***
## Expt.No.CPT:2007:Brookings          ***
## Expt.No.CPT:2007:DLakesPea          ***
## Expt.No.CPT:2007:Hayes              .  
## Expt.No.CPT:2007:Kennebec           ***
## Expt.No.CPT:2007:Martin             ***
## Expt.No.CPT:2007:Onida              ***
## Expt.No.CPT:2007:Platte             ***
## Expt.No.CPT:2007:Selby              ***
## Expt.No.CPT:2007:Sturgis            ** 
## Expt.No.CPT:2007:Wall               ***
## Expt.No.CPT:2007:Watertown          *  
## Expt.No.CPT:2007:Winner             ***
## Expt.No.CPT:2007:Bison:Rep.No.2        
## Expt.No.CPT:2007:Brookings:Rep.No.2 *  
## Expt.No.CPT:2007:DLakesPea:Rep.No.2    
## Expt.No.CPT:2007:Hayes:Rep.No.2        
## Expt.No.CPT:2007:Kennebec:Rep.No.2  ***
## Expt.No.CPT:2007:Martin:Rep.No.2       
## Expt.No.CPT:2007:Onida:Rep.No.2        
## Expt.No.CPT:2007:Platte:Rep.No.2    .  
## Expt.No.CPT:2007:Selby:Rep.No.2        
## Expt.No.CPT:2007:Sturgis:Rep.No.2      
## Expt.No.CPT:2007:Wall:Rep.No.2         
## Expt.No.CPT:2007:Watertown:Rep.No.2 *  
## Expt.No.CPT:2007:Winner:Rep.No.2       
## Expt.No.CPT:2007:Bison:Rep.No.3        
## Expt.No.CPT:2007:Brookings:Rep.No.3 ** 
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 .  
## Expt.No.CPT:2007:Hayes:Rep.No.3        
## Expt.No.CPT:2007:Kennebec:Rep.No.3  .  
## Expt.No.CPT:2007:Martin:Rep.No.3       
## Expt.No.CPT:2007:Onida:Rep.No.3        
## Expt.No.CPT:2007:Platte:Rep.No.3    *  
## Expt.No.CPT:2007:Selby:Rep.No.3        
## Expt.No.CPT:2007:Sturgis:Rep.No.3      
## Expt.No.CPT:2007:Wall:Rep.No.3         
## Expt.No.CPT:2007:Watertown:Rep.No.3 .  
## Expt.No.CPT:2007:Winner:Rep.No.3       
## Expt.No.CPT:2007:Bison:Rep.No.4     .  
## Expt.No.CPT:2007:Brookings:Rep.No.4 ** 
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 .  
## Expt.No.CPT:2007:Hayes:Rep.No.4        
## Expt.No.CPT:2007:Kennebec:Rep.No.4     
## Expt.No.CPT:2007:Martin:Rep.No.4       
## Expt.No.CPT:2007:Onida:Rep.No.4        
## Expt.No.CPT:2007:Platte:Rep.No.4    .  
## Expt.No.CPT:2007:Selby:Rep.No.4        
## Expt.No.CPT:2007:Sturgis:Rep.No.4      
## Expt.No.CPT:2007:Wall:Rep.No.4         
## Expt.No.CPT:2007:Watertown:Rep.No.4 .  
## Expt.No.CPT:2007:Winner:Rep.No.4       
## eCriteria.Entry.No.:eExpt.No.       ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.992 on 556 degrees of freedom
## Multiple R-squared:  0.7495, Adjusted R-squared:  0.7211 
## F-statistic: 26.41 on 63 and 556 DF,  p-value: < 2.2e-16
## 
##                                Df Sum Sq Mean Sq F value   Pr(>F)    
## Criteria.Entry.No.             11   1870   170.0  42.823  < 2e-16 ***
## Expt.No.                       12   4139   344.9  86.887  < 2e-16 ***
## Expt.No.:Rep.No.               39    298     7.6   1.926 0.000836 ***
## eCriteria.Entry.No.:eExpt.No.   1    297   296.6  74.733  < 2e-16 ***
## Residuals                     556   2207     4.0                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3509 -1.2308  0.0412  1.2838  5.5421 
## 
## Coefficients: (1 not defined because of singularities)
##                                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)                          65.329779   0.766828  85.195  < 2e-16
## Criteria.Entry.No.02                 -0.118485   0.376960  -0.314 0.753400
## Criteria.Entry.No.03                 -1.737277   0.376960  -4.609 5.05e-06
## Criteria.Entry.No.04                 -1.979005   0.383228  -5.164 3.39e-07
## Criteria.Entry.No.05                  1.613712   0.376960   4.281 2.20e-05
## Criteria.Entry.No.06                  1.451176   0.376960   3.850 0.000132
## Criteria.Entry.No.07                  0.155642   0.376960   0.413 0.679851
## Criteria.Entry.No.08                 -3.810553   0.376960 -10.109  < 2e-16
## Criteria.Entry.No.09                  1.544652   0.376960   4.098 4.81e-05
## Criteria.Entry.No.10                 -1.934556   0.376960  -5.132 3.99e-07
## Criteria.Entry.No.11                 -1.368763   0.379078  -3.611 0.000333
## Criteria.Entry.No.12                 -3.010010   0.376960  -7.985 8.36e-15
## Expt.No.CPT:2007:Brookings           -4.956078   0.880126  -5.631 2.87e-08
## Expt.No.CPT:2007:DLakesPea           -7.143120   0.972503  -7.345 7.53e-13
## Expt.No.CPT:2007:Hayes               -1.985627   0.800789  -2.480 0.013454
## Expt.No.CPT:2007:Kennebec           -10.800853   1.185433  -9.111  < 2e-16
## Expt.No.CPT:2007:Martin              -4.313602   0.857965  -5.028 6.74e-07
## Expt.No.CPT:2007:Onida               -7.939082   1.011634  -7.848 2.25e-14
## Expt.No.CPT:2007:Platte              -8.466301   1.038914  -8.149 2.51e-15
## Expt.No.CPT:2007:Selby              -10.198019   1.135068  -8.985  < 2e-16
## Expt.No.CPT:2007:Sturgis             -3.092557   0.823175  -3.757 0.000191
## Expt.No.CPT:2007:Wall                -5.577122   0.903842  -6.170 1.33e-09
## Expt.No.CPT:2007:Watertown           -2.638942   0.812898  -3.246 0.001241
## Expt.No.CPT:2007:Winner              -7.762773   1.002745  -7.742 4.79e-14
## Expt.No.CPT:2007:Bison:Rep.No.2      -0.858333   0.784706  -1.094 0.274513
## Expt.No.CPT:2007:Brookings:Rep.No.2  -1.684051   0.802722  -2.098 0.036371
## Expt.No.CPT:2007:DLakesPea:Rep.No.2  -0.034167   0.784706  -0.044 0.965286
## Expt.No.CPT:2007:Hayes:Rep.No.2       0.033333   0.784706   0.042 0.966132
## Expt.No.CPT:2007:Kennebec:Rep.No.2   -3.654318   0.803637  -4.547 6.70e-06
## Expt.No.CPT:2007:Martin:Rep.No.2      0.725000   0.784706   0.924 0.355940
## Expt.No.CPT:2007:Onida:Rep.No.2       0.110000   0.784706   0.140 0.888570
## Expt.No.CPT:2007:Platte:Rep.No.2     -1.345416   0.784706  -1.715 0.086996
## Expt.No.CPT:2007:Selby:Rep.No.2       0.657084   0.784706   0.837 0.402754
## Expt.No.CPT:2007:Sturgis:Rep.No.2     1.175000   0.784706   1.497 0.134873
## Expt.No.CPT:2007:Wall:Rep.No.2        0.491033   0.784706   0.626 0.531738
## Expt.No.CPT:2007:Watertown:Rep.No.2  -1.622501   0.784706  -2.068 0.039143
## Expt.No.CPT:2007:Winner:Rep.No.2      0.115834   0.784706   0.148 0.882702
## Expt.No.CPT:2007:Bison:Rep.No.3      -0.275000   0.784706  -0.350 0.726136
## Expt.No.CPT:2007:Brookings:Rep.No.3  -2.669507   0.802722  -3.326 0.000942
## Expt.No.CPT:2007:DLakesPea:Rep.No.3  -1.425000   0.784706  -1.816 0.069924
## Expt.No.CPT:2007:Hayes:Rep.No.3       0.850000   0.784706   1.083 0.279194
## Expt.No.CPT:2007:Kennebec:Rep.No.3   -1.656819   0.803637  -2.062 0.039714
## Expt.No.CPT:2007:Martin:Rep.No.3      0.108333   0.784706   0.138 0.890247
## Expt.No.CPT:2007:Onida:Rep.No.3      -0.596667   0.784706  -0.760 0.447361
## Expt.No.CPT:2007:Platte:Rep.No.3     -1.854167   0.784706  -2.363 0.018483
## Expt.No.CPT:2007:Selby:Rep.No.3      -0.132915   0.784706  -0.169 0.865558
## Expt.No.CPT:2007:Sturgis:Rep.No.3     0.391667   0.784706   0.499 0.617892
## Expt.No.CPT:2007:Wall:Rep.No.3       -0.312079   0.784706  -0.398 0.691005
## Expt.No.CPT:2007:Watertown:Rep.No.3  -1.453334   0.784706  -1.852 0.064554
## Expt.No.CPT:2007:Winner:Rep.No.3     -0.060000   0.784706  -0.076 0.939080
## Expt.No.CPT:2007:Bison:Rep.No.4       1.391667   0.784706   1.773 0.076705
## Expt.No.CPT:2007:Brookings:Rep.No.4  -2.276780   0.802722  -2.836 0.004733
## Expt.No.CPT:2007:DLakesPea:Rep.No.4  -1.358750   0.784706  -1.732 0.083920
## Expt.No.CPT:2007:Hayes:Rep.No.4       0.291667   0.784706   0.372 0.710268
## Expt.No.CPT:2007:Kennebec:Rep.No.4   -0.340819   0.803637  -0.424 0.671663
## Expt.No.CPT:2007:Martin:Rep.No.4     -0.866667   0.784706  -1.104 0.269885
## Expt.No.CPT:2007:Onida:Rep.No.4       0.572500   0.784706   0.730 0.465964
## Expt.No.CPT:2007:Platte:Rep.No.4     -1.565417   0.784706  -1.995 0.046549
## Expt.No.CPT:2007:Selby:Rep.No.4       0.922501   0.784706   1.176 0.240267
## Expt.No.CPT:2007:Sturgis:Rep.No.4     0.016668   0.784706   0.021 0.983061
## Expt.No.CPT:2007:Wall:Rep.No.4       -0.042300   0.784706  -0.054 0.957030
## Expt.No.CPT:2007:Watertown:Rep.No.4  -1.364168   0.784706  -1.738 0.082696
## Expt.No.CPT:2007:Winner:Rep.No.4      0.232084   0.784706   0.296 0.767526
## Criteria.Entry.No.01:eExpt.No.       -0.372964   0.162631  -2.293 0.022209
## Criteria.Entry.No.02:eExpt.No.       -0.831938   0.162631  -5.115 4.34e-07
## Criteria.Entry.No.03:eExpt.No.        0.140961   0.162631   0.867 0.386459
## Criteria.Entry.No.04:eExpt.No.        0.322587   0.162808   1.981 0.048049
## Criteria.Entry.No.05:eExpt.No.       -0.758626   0.162631  -4.665 3.89e-06
## Criteria.Entry.No.06:eExpt.No.       -0.805206   0.162631  -4.951 9.85e-07
## Criteria.Entry.No.07:eExpt.No.       -0.422445   0.162631  -2.598 0.009642
## Criteria.Entry.No.08:eExpt.No.       -0.005928   0.162631  -0.036 0.970936
## Criteria.Entry.No.09:eExpt.No.       -0.723582   0.162631  -4.449 1.04e-05
## Criteria.Entry.No.10:eExpt.No.       -0.372377   0.162631  -2.290 0.022419
## Criteria.Entry.No.11:eExpt.No.       -0.603251   0.164994  -3.656 0.000281
## Criteria.Entry.No.12:eExpt.No.              NA         NA      NA       NA
##                                        
## (Intercept)                         ***
## Criteria.Entry.No.02                   
## Criteria.Entry.No.03                ***
## Criteria.Entry.No.04                ***
## Criteria.Entry.No.05                ***
## Criteria.Entry.No.06                ***
## Criteria.Entry.No.07                   
## Criteria.Entry.No.08                ***
## Criteria.Entry.No.09                ***
## Criteria.Entry.No.10                ***
## Criteria.Entry.No.11                ***
## Criteria.Entry.No.12                ***
## Expt.No.CPT:2007:Brookings          ***
## Expt.No.CPT:2007:DLakesPea          ***
## Expt.No.CPT:2007:Hayes              *  
## Expt.No.CPT:2007:Kennebec           ***
## Expt.No.CPT:2007:Martin             ***
## Expt.No.CPT:2007:Onida              ***
## Expt.No.CPT:2007:Platte             ***
## Expt.No.CPT:2007:Selby              ***
## Expt.No.CPT:2007:Sturgis            ***
## Expt.No.CPT:2007:Wall               ***
## Expt.No.CPT:2007:Watertown          ** 
## Expt.No.CPT:2007:Winner             ***
## Expt.No.CPT:2007:Bison:Rep.No.2        
## Expt.No.CPT:2007:Brookings:Rep.No.2 *  
## Expt.No.CPT:2007:DLakesPea:Rep.No.2    
## Expt.No.CPT:2007:Hayes:Rep.No.2        
## Expt.No.CPT:2007:Kennebec:Rep.No.2  ***
## Expt.No.CPT:2007:Martin:Rep.No.2       
## Expt.No.CPT:2007:Onida:Rep.No.2        
## Expt.No.CPT:2007:Platte:Rep.No.2    .  
## Expt.No.CPT:2007:Selby:Rep.No.2        
## Expt.No.CPT:2007:Sturgis:Rep.No.2      
## Expt.No.CPT:2007:Wall:Rep.No.2         
## Expt.No.CPT:2007:Watertown:Rep.No.2 *  
## Expt.No.CPT:2007:Winner:Rep.No.2       
## Expt.No.CPT:2007:Bison:Rep.No.3        
## Expt.No.CPT:2007:Brookings:Rep.No.3 ***
## Expt.No.CPT:2007:DLakesPea:Rep.No.3 .  
## Expt.No.CPT:2007:Hayes:Rep.No.3        
## Expt.No.CPT:2007:Kennebec:Rep.No.3  *  
## Expt.No.CPT:2007:Martin:Rep.No.3       
## Expt.No.CPT:2007:Onida:Rep.No.3        
## Expt.No.CPT:2007:Platte:Rep.No.3    *  
## Expt.No.CPT:2007:Selby:Rep.No.3        
## Expt.No.CPT:2007:Sturgis:Rep.No.3      
## Expt.No.CPT:2007:Wall:Rep.No.3         
## Expt.No.CPT:2007:Watertown:Rep.No.3 .  
## Expt.No.CPT:2007:Winner:Rep.No.3       
## Expt.No.CPT:2007:Bison:Rep.No.4     .  
## Expt.No.CPT:2007:Brookings:Rep.No.4 ** 
## Expt.No.CPT:2007:DLakesPea:Rep.No.4 .  
## Expt.No.CPT:2007:Hayes:Rep.No.4        
## Expt.No.CPT:2007:Kennebec:Rep.No.4     
## Expt.No.CPT:2007:Martin:Rep.No.4       
## Expt.No.CPT:2007:Onida:Rep.No.4        
## Expt.No.CPT:2007:Platte:Rep.No.4    *  
## Expt.No.CPT:2007:Selby:Rep.No.4        
## Expt.No.CPT:2007:Sturgis:Rep.No.4      
## Expt.No.CPT:2007:Wall:Rep.No.4         
## Expt.No.CPT:2007:Watertown:Rep.No.4 .  
## Expt.No.CPT:2007:Winner:Rep.No.4       
## Criteria.Entry.No.01:eExpt.No.      *  
## Criteria.Entry.No.02:eExpt.No.      ***
## Criteria.Entry.No.03:eExpt.No.         
## Criteria.Entry.No.04:eExpt.No.      *  
## Criteria.Entry.No.05:eExpt.No.      ***
## Criteria.Entry.No.06:eExpt.No.      ***
## Criteria.Entry.No.07:eExpt.No.      ** 
## Criteria.Entry.No.08:eExpt.No.         
## Criteria.Entry.No.09:eExpt.No.      ***
## Criteria.Entry.No.10:eExpt.No.      *  
## Criteria.Entry.No.11:eExpt.No.      ***
## Criteria.Entry.No.12:eExpt.No.         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.922 on 546 degrees of freedom
## Multiple R-squared:  0.771,  Adjusted R-squared:  0.7404 
## F-statistic: 25.19 on 73 and 546 DF,  p-value: < 2.2e-16
## 
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.113 -1.148  0.172  1.295  4.875 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## Criteria.Entry.No.01            59.1978     0.2892 204.664  < 2e-16 ***
## Criteria.Entry.No.02            59.0793     0.2892 204.254  < 2e-16 ***
## Criteria.Entry.No.03            57.4606     0.2892 198.657  < 2e-16 ***
## Criteria.Entry.No.04            57.3348     0.2980 192.397  < 2e-16 ***
## Criteria.Entry.No.05            60.8115     0.2892 210.243  < 2e-16 ***
## Criteria.Entry.No.06            60.6490     0.2892 209.681  < 2e-16 ***
## Criteria.Entry.No.07            59.3535     0.2892 205.202  < 2e-16 ***
## Criteria.Entry.No.08            55.3873     0.2892 191.489  < 2e-16 ***
## Criteria.Entry.No.09            60.7425     0.2892 210.004  < 2e-16 ***
## Criteria.Entry.No.10            57.2633     0.2892 197.975  < 2e-16 ***
## Criteria.Entry.No.11            57.8142     0.2922 197.847  < 2e-16 ***
## Criteria.Entry.No.12            56.1878     0.2892 194.257  < 2e-16 ***
## Criteria.Entry.No.01:eExpt.No.   1.0689     0.1248   8.566  < 2e-16 ***
## Criteria.Entry.No.02:eExpt.No.   0.6099     0.1248   4.888 1.31e-06 ***
## Criteria.Entry.No.03:eExpt.No.   1.5828     0.1248  12.684  < 2e-16 ***
## Criteria.Entry.No.04:eExpt.No.   1.7772     0.1250  14.214  < 2e-16 ***
## Criteria.Entry.No.05:eExpt.No.   0.6832     0.1248   5.475 6.45e-08 ***
## Criteria.Entry.No.06:eExpt.No.   0.6367     0.1248   5.102 4.52e-07 ***
## Criteria.Entry.No.07:eExpt.No.   1.0194     0.1248   8.169 1.86e-15 ***
## Criteria.Entry.No.08:eExpt.No.   1.4359     0.1248  11.507  < 2e-16 ***
## Criteria.Entry.No.09:eExpt.No.   0.7183     0.1248   5.756 1.38e-08 ***
## Criteria.Entry.No.10:eExpt.No.   1.0695     0.1248   8.571  < 2e-16 ***
## Criteria.Entry.No.11:eExpt.No.   0.8489     0.1281   6.627 7.65e-11 ***
## Criteria.Entry.No.12:eExpt.No.   1.4419     0.1248  11.555  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.086 on 596 degrees of freedom
## Multiple R-squared:  0.9988, Adjusted R-squared:  0.9987 
## F-statistic: 2.034e+04 on 24 and 596 DF,  p-value: < 2.2e-16
## 
##                               Df  Sum Sq Mean Sq  F value Pr(>F)    
## Criteria.Entry.No.            12 2119788  176649 40604.79 <2e-16 ***
## Criteria.Entry.No.:eExpt.No.  12    4348     362    83.28 <2e-16 ***
## Residuals                    596    2593       4                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##                               Df Sum Sq Mean Sq F value   Pr(>F)    
## Criteria.Entry.No.            11   1870   170.0  46.009  < 2e-16 ***
## Expt.No.                      12   4139   344.9  93.351  < 2e-16 ***
## Expt.No.:Rep.No.              39    298     7.6   2.069 0.000221 ***
## Criteria.Entry.No.:eExpt.No.  11    486    44.2  11.969  < 2e-16 ***
## Residuals                    546   2017     3.7                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 4.287986
## [1] Interaction sd Value:
## [1] 1.769951
## [1] Error sd Value:
## [1] 1.429329
## [1] Pairs:
## [[1]]
## [1] 4 9
## 
## [[2]]
## [1]  4 10
## 
## [1] 
## [1]
tw.res$cluster$score/tw.res$add.cluster$score
##  [1] 1.2259743 0.9697232 0.9300447 0.8278188 0.8531172 0.8305974 0.8721844
##  [8] 3.7475335 1.8639613 0.2142266 1.6386883 0.9722437
tw.res <- standard.sensitivity.plot(tw.dat,
                                     response = response,
                          TreatmentName = TreatmentName,
                          TrialName = TrialName,
                          RepName=RepName,
                          dual.dendrogram=FALSE,
                          plot.outliers=TRUE,legend.columns=3)

gy.matrix <- data.frame(tapply(gy.dat$Plot.Mean,list(gy.dat$Expt.No.,gy.dat$Criteria.Entry.No.),mean))
gy.vector <- tapply(gy.dat$Plot.Mean,list(gy.dat$Expt.No.),mean)

par(fig = c(0, 1, 0.4, 1), mar = (c(1, 4, 1, 1) + 0.1))
gy.table <- plot.interaction.ARMST(gy.matrix, gy.vector, ylab='Treatment in Trial Mean',
                      regression=TRUE, main='GY', show.legend=TRUE,legend.pos=c(.01,.98),
                      legend.columns=4,lwd = 1
                      )
par(fig=c(0,1,0,.4),mar=(c(5, 4, 0, 1) + 0.1), new=TRUE)
gy.hc <- plot.clusters.ARMST(gy.matrix, gy.vector, xlab='Trial Mean', ylab='')

par(fig = c(0, 1, 0, 1))

tw.matrix <- data.frame(tapply(tw.dat$Plot.Mean,list(tw.dat$Expt.No.,tw.dat$Criteria.Entry.No.),mean))
tw.vector <- tapply(tw.dat$Plot.Mean,list(tw.dat$Expt.No.),mean)
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 4, 1, 1) + 0.1))
tw.table <- plot.interaction.ARMST(tw.matrix, tw.vector, ylab='Treatment in Trial Mean',
                      regression=TRUE, main='TW', show.legend=TRUE,legend.pos=c(.01,.98),
                      legend.columns=4,lwd = 1
                      )
par(fig=c(0,1,0,.4),mar=(c(5, 4, 0, 1) + 0.1), new=TRUE)
tw.hc <- plot.clusters.ARMST(tw.matrix, tw.vector, xlab='Trial Mean', ylab='')

par(fig = c(0, 1, 0, 1))

hd.matrix <- data.frame(tapply(hd.dat$Plot.Mean,list(hd.dat$Expt.No.,hd.dat$Criteria.Entry.No.),mean))
hd.vector <- tapply(hd.dat$Plot.Mean,list(hd.dat$Expt.No.),mean)
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 4, 1, 1) + 0.1))
hd.table <- plot.interaction.ARMST(hd.matrix, hd.vector, ylab='Treatment in Trial Mean',
                      regression=TRUE, main='HD', show.legend=TRUE,legend.pos=c(.01,.98),
                      legend.columns=4,lwd = 1
                      )
par(fig=c(0,1,0,.4),mar=(c(5, 4, 0, 1) + 0.1), new=TRUE)
hd.hc <- plot.clusters.ARMST(hd.matrix, hd.vector, xlab='Trial Mean', ylab='')

par(fig = c(0, 1, 0, 1))


ht.matrix <- data.frame(tapply(ht.dat$Plot.Mean,list(ht.dat$Expt.No.,ht.dat$Criteria.Entry.No.),mean))
ht.vector <- tapply(ht.dat$Plot.Mean,list(ht.dat$Expt.No.),mean)
par(fig = c(0, 1, 0.4, 1), mar = (c(1, 4, 1, 1) + 0.1))
ht.table <- plot.interaction.ARMST(ht.matrix, ht.vector, ylab='Treatment in Trial Mean',
                      regression=TRUE, main='HT', show.legend=TRUE,legend.pos=c(.01,.98),
                      legend.columns=4,lwd = 1
                      )
par(fig=c(0,1,0,.4),mar=(c(5, 4, 0, 1) + 0.1), new=TRUE)
ht.hc <- plot.clusters.ARMST(ht.matrix, ht.vector, xlab='Trial Mean', ylab='')

par(fig = c(0, 1, 0, 1))
library(agridat)
data(pacheco.soybean)
mixed.res <- standard.sensitivity.plot(pacheco.soybean,
                                     response = "yield",
                          TreatmentName = "gen",
                          TrialName = "env",
                          dual.dendrogram=TRUE,
                          plot.outliers=TRUE,legend.columns=3)
## Warning in anova.lm(base.lm): ANOVA F-tests on an essentially perfect fit
## are unreliable

print.stdplot(mixed.res)
## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
## 
## Response: yield
##            Df   Sum Sq Mean Sq F value Pr(>F)
## env        10 35202247 3520225               
## gen        17  5599202  329365               
## env:gen   170 13110345   77120               
## Residuals   0        0                       
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula: yield ~ env + (1 | env)
##    Data: plot.dat
## 
## REML criterion at convergence: 2715.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4589 -0.5756 -0.1965  0.5819  3.8459 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  env      (Intercept) 271980   521.5   
##  Residual             100051   316.3   
## Number of obs: 198, groups:  env, 11
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)  1756.28     526.82   3.334
## envE10        533.11     745.04   0.716
## envE11        429.11     745.04   0.576
## envE2          14.67     745.04   0.020
## envE3         -97.11     745.04  -0.130
## envE4        -631.00     745.04  -0.847
## envE5         836.22     745.04   1.122
## envE6         644.00     745.04   0.864
## envE7         639.39     745.04   0.858
## envE8         700.50     745.04   0.940
## envE9         399.61     745.04   0.536
## 
## Correlation of Fixed Effects:
##        (Intr) envE10 envE11 envE2  envE3  envE4  envE5  envE6  envE7 
## envE10 -0.707                                                        
## envE11 -0.707  0.500                                                 
## envE2  -0.707  0.500  0.500                                          
## envE3  -0.707  0.500  0.500  0.500                                   
## envE4  -0.707  0.500  0.500  0.500  0.500                            
## envE5  -0.707  0.500  0.500  0.500  0.500  0.500                     
## envE6  -0.707  0.500  0.500  0.500  0.500  0.500  0.500              
## envE7  -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500       
## envE8  -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE9  -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
##        envE8 
## envE10       
## envE11       
## envE2        
## envE3        
## envE4        
## envE5        
## envE6        
## envE7        
## envE8        
## envE9   0.500
## [1] 
## [1] Stability
## [1] ----------------------------------------------------
##    Treatment     Slope    Intercept     Mean       SD           b
## 1          1 1.4371854  -519.540201 2457.727 694.3969  0.43718540
## 2          2 0.7529629   493.528808 2053.364 359.1532 -0.24703714
## 3          3 0.8678092   383.704504 2181.455 416.4498 -0.13219080
## 4          4 0.8428426   426.425245 2172.455 384.2841 -0.15715741
## 5          5 0.8954853  -118.720032 1736.364 466.4005 -0.10451473
## 6          6 0.9302448   246.726824 2173.818 421.7411 -0.06975521
## 7          7 1.0134538  -239.466888 1860.000 496.6359  0.01345384
## 8          8 0.9013210    -9.082115 1858.091 405.0497 -0.09867896
## 9          9 0.9022891   184.003401 2053.182 514.3938 -0.09771092
## 10        10 1.3039264  -475.481353 2225.727 668.1058  0.30392638
## 11        11 1.2222482  -273.640708 2258.364 582.7875  0.22224816
## 12        12 1.6135014 -1260.977590 2081.545 883.7377  0.61350143
## 13        13 0.7168354   600.461273 2085.455 385.8571 -0.28316462
## 14        14 0.6847354   502.414077 1920.909 389.6428 -0.31526464
## 15        15 0.9749964   139.746846 2159.545 493.3809 -0.02500360
## 16        16 0.8326711   392.405551 2117.364 423.7561 -0.16732890
## 17        17 1.1997387  -562.646605 1922.727 581.8160  0.19973872
## 18        18 0.9077530    90.138965 1970.636 446.1592 -0.09224702
##            Pb          bR2
## 1  0.06796959 0.3232929699
## 2  0.03765971 0.3971957795
## 3  0.30632842 0.1155826760
## 4  0.05274230 0.3557873414
## 5  0.58732928 0.0339946462
## 6  0.34517872 0.0993177085
## 7  0.93534335 0.0007726187
## 8  0.10254631 0.2684395841
## 9  0.69896421 0.0174093261
## 10 0.26263331 0.1369371880
## 11 0.20902670 0.1690456689
## 12 0.15294373 0.2130733625
## 13 0.12186434 0.2447356318
## 14 0.12224680 0.2443021526
## 15 0.89303284 0.0021213177
## 16 0.31735312 0.1107330241
## 17 0.29586138 0.1203746658
## 18 0.54524179 0.0420556074
## [1] 
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = as.formula(modelString), data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -646.71 -134.68  -18.94  139.41  859.00 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.142e+03  1.034e+02  20.727  < 2e-16 ***
## genG10      -4.044e+02  1.172e+02  -3.450 0.000708 ***
## genG11      -2.763e+02  1.172e+02  -2.357 0.019556 *  
## genG12      -2.853e+02  1.172e+02  -2.434 0.015972 *  
## genG13      -7.214e+02  1.172e+02  -6.155 5.28e-09 ***
## genG14      -2.839e+02  1.172e+02  -2.422 0.016475 *  
## genG15      -5.977e+02  1.172e+02  -5.100 9.04e-07 ***
## genG16      -5.996e+02  1.172e+02  -5.116 8.39e-07 ***
## genG17      -4.045e+02  1.172e+02  -3.452 0.000704 ***
## genG18      -2.320e+02  1.172e+02  -1.980 0.049383 *  
## genG2       -1.994e+02  1.172e+02  -1.701 0.090775 .  
## genG3       -3.762e+02  1.172e+02  -3.210 0.001590 ** 
## genG4       -3.723e+02  1.172e+02  -3.176 0.001773 ** 
## genG5       -5.368e+02  1.172e+02  -4.580 8.98e-06 ***
## genG6       -2.982e+02  1.172e+02  -2.544 0.011849 *  
## genG7       -3.404e+02  1.172e+02  -2.904 0.004175 ** 
## genG8       -5.350e+02  1.172e+02  -4.565 9.59e-06 ***
## genG9       -4.871e+02  1.172e+02  -4.156 5.14e-05 ***
## envE10       5.331e+02  9.162e+01   5.819 2.90e-08 ***
## envE11       4.291e+02  9.162e+01   4.684 5.77e-06 ***
## envE2        1.467e+01  9.162e+01   0.160 0.873009    
## envE3       -9.711e+01  9.162e+01  -1.060 0.290689    
## envE4       -6.310e+02  9.162e+01  -6.887 1.07e-10 ***
## envE5        8.362e+02  9.162e+01   9.127  < 2e-16 ***
## envE6        6.440e+02  9.162e+01   7.029 4.90e-11 ***
## envE7        6.394e+02  9.162e+01   6.979 6.47e-11 ***
## envE8        7.005e+02  9.162e+01   7.646 1.49e-12 ***
## envE9        3.996e+02  9.162e+01   4.362 2.24e-05 ***
## egen:eenv    5.867e-04  2.755e-04   2.130 0.034651 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 274.9 on 169 degrees of freedom
## Multiple R-squared:  0.7632, Adjusted R-squared:  0.7239 
## F-statistic: 19.45 on 28 and 169 DF,  p-value: < 2.2e-16
## 
##              Df   Sum Sq Mean Sq F value  Pr(>F)    
## gen          17  5599202  329365   4.360 2.2e-07 ***
## env          10 35202247 3520225  46.596 < 2e-16 ***
## egen:eenv     1   342634  342634   4.535  0.0347 *  
## Residuals   169 12767711   75549                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -701.95 -113.36  -18.73  113.68  832.25 
## 
## Coefficients: (1 not defined because of singularities)
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.171e+03  1.164e+02  18.649  < 2e-16 ***
## genG10      -4.044e+02  1.141e+02  -3.545 0.000522 ***
## genG11      -2.763e+02  1.141e+02  -2.422 0.016611 *  
## genG12      -2.853e+02  1.141e+02  -2.501 0.013447 *  
## genG13      -7.214e+02  1.141e+02  -6.324 2.67e-09 ***
## genG14      -2.839e+02  1.141e+02  -2.489 0.013889 *  
## genG15      -5.977e+02  1.141e+02  -5.240 5.25e-07 ***
## genG16      -5.996e+02  1.141e+02  -5.256 4.86e-07 ***
## genG17      -4.045e+02  1.141e+02  -3.546 0.000519 ***
## genG18      -2.320e+02  1.141e+02  -2.034 0.043706 *  
## genG2       -1.994e+02  1.141e+02  -1.748 0.082531 .  
## genG3       -3.762e+02  1.141e+02  -3.298 0.001212 ** 
## genG4       -3.723e+02  1.141e+02  -3.263 0.001358 ** 
## genG5       -5.368e+02  1.141e+02  -4.706 5.62e-06 ***
## genG6       -2.982e+02  1.141e+02  -2.614 0.009846 ** 
## genG7       -3.404e+02  1.141e+02  -2.984 0.003316 ** 
## genG8       -5.350e+02  1.141e+02  -4.690 6.01e-06 ***
## genG9       -4.871e+02  1.141e+02  -4.270 3.42e-05 ***
## envE10       4.839e+02  1.333e+02   3.630 0.000386 ***
## envE11       3.895e+02  1.197e+02   3.255 0.001394 ** 
## envE2        1.331e+01  8.922e+01   0.149 0.881572    
## envE3       -8.815e+01  9.099e+01  -0.969 0.334144    
## envE4       -5.728e+02  1.474e+02  -3.887 0.000151 ***
## envE5        7.591e+02  1.792e+02   4.235 3.93e-05 ***
## envE6        5.846e+02  1.493e+02   3.916 0.000135 ***
## envE7        5.804e+02  1.486e+02   3.906 0.000141 ***
## envE8        6.359e+02  1.578e+02   4.029 8.81e-05 ***
## envE9        3.627e+02  1.161e+02   3.125 0.002126 ** 
## genG1:eenv   5.294e-01  2.705e-01   1.957 0.052178 .  
## genG10:eenv -1.548e-01  2.705e-01  -0.572 0.568066    
## genG11:eenv -3.994e-02  2.705e-01  -0.148 0.882820    
## genG12:eenv -6.491e-02  2.705e-01  -0.240 0.810710    
## genG13:eenv -1.227e-02  2.705e-01  -0.045 0.963892    
## genG14:eenv  2.249e-02  2.705e-01   0.083 0.933853    
## genG15:eenv  1.057e-01  2.705e-01   0.391 0.696565    
## genG16:eenv -6.432e-03  2.705e-01  -0.024 0.981064    
## genG17:eenv -5.464e-03  2.705e-01  -0.020 0.983913    
## genG18:eenv  3.962e-01  2.705e-01   1.464 0.145149    
## genG2:eenv   3.145e-01  2.705e-01   1.162 0.246862    
## genG3:eenv   7.057e-01  2.705e-01   2.609 0.009992 ** 
## genG4:eenv  -1.909e-01  2.705e-01  -0.706 0.481462    
## genG5:eenv  -2.230e-01  2.705e-01  -0.824 0.411037    
## genG6:eenv   6.724e-02  2.705e-01   0.249 0.804044    
## genG7:eenv  -7.508e-02  2.705e-01  -0.278 0.781755    
## genG8:eenv   2.920e-01  2.705e-01   1.079 0.282175    
## genG9:eenv          NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 267.5 on 153 degrees of freedom
## Multiple R-squared:  0.7969, Adjusted R-squared:  0.7385 
## F-statistic: 13.64 on 44 and 153 DF,  p-value: < 2.2e-16
## 
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -701.95 -113.36  -18.73  113.68  832.25 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## genG1       2457.7273    78.3908  31.352  < 2e-16 ***
## genG10      2053.3636    78.3908  26.194  < 2e-16 ***
## genG11      2181.4545    78.3908  27.828  < 2e-16 ***
## genG12      2172.4545    78.3908  27.713  < 2e-16 ***
## genG13      1736.3636    78.3908  22.150  < 2e-16 ***
## genG14      2173.8182    78.3908  27.731  < 2e-16 ***
## genG15      1860.0000    78.3908  23.727  < 2e-16 ***
## genG16      1858.0909    78.3908  23.703  < 2e-16 ***
## genG17      2053.1818    78.3908  26.192  < 2e-16 ***
## genG18      2225.7273    78.3908  28.393  < 2e-16 ***
## genG2       2258.3636    78.3908  28.809  < 2e-16 ***
## genG3       2081.5455    78.3908  26.553  < 2e-16 ***
## genG4       2085.4545    78.3908  26.603  < 2e-16 ***
## genG5       1920.9091    78.3908  24.504  < 2e-16 ***
## genG6       2159.5455    78.3908  27.548  < 2e-16 ***
## genG7       2117.3636    78.3908  27.010  < 2e-16 ***
## genG8       1922.7273    78.3908  24.527  < 2e-16 ***
## genG9       1970.6364    78.3908  25.139  < 2e-16 ***
## genG1:eenv     1.4372     0.1859   7.730 1.07e-12 ***
## genG10:eenv    0.7530     0.1859   4.050 7.92e-05 ***
## genG11:eenv    0.8678     0.1859   4.668 6.35e-06 ***
## genG12:eenv    0.8428     0.1859   4.534 1.12e-05 ***
## genG13:eenv    0.8955     0.1859   4.817 3.33e-06 ***
## genG14:eenv    0.9302     0.1859   5.004 1.45e-06 ***
## genG15:eenv    1.0135     0.1859   5.451 1.83e-07 ***
## genG16:eenv    0.9013     0.1859   4.848 2.90e-06 ***
## genG17:eenv    0.9023     0.1859   4.853 2.84e-06 ***
## genG18:eenv    1.3039     0.1859   7.014 5.98e-11 ***
## genG2:eenv     1.2222     0.1859   6.574 6.40e-10 ***
## genG3:eenv     1.6135     0.1859   8.679 4.06e-15 ***
## genG4:eenv     0.7168     0.1859   3.856 0.000166 ***
## genG5:eenv     0.6847     0.1859   3.683 0.000314 ***
## genG6:eenv     0.9750     0.1859   5.244 4.84e-07 ***
## genG7:eenv     0.8327     0.1859   4.479 1.41e-05 ***
## genG8:eenv     1.1997     0.1859   6.453 1.21e-09 ***
## genG9:eenv     0.9078     0.1859   4.883 2.49e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 260 on 162 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9852 
## F-statistic: 366.8 on 36 and 162 DF,  p-value: < 2.2e-16
## 
##            Df    Sum Sq  Mean Sq F value Pr(>F)    
## gen        18 855318146 47517675  702.96 <2e-16 ***
## gen:eenv   18  37361982  2075666   30.71 <2e-16 ***
## Residuals 162  10950610    67596                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##              Df   Sum Sq Mean Sq F value   Pr(>F)    
## gen          17  5599202  329365   4.602 9.83e-08 ***
## env          10 35202247 3520225  49.184  < 2e-16 ***
## gen:eenv     17  2159734  127043   1.775   0.0359 *  
## Residuals   153 10950610   71573                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 835.5741
## [1] Interaction sd Value:
## [1] 278.5247
## [1] Error sd Value:
## [1] NaN
## [1] Pairs:
## [[1]]
## [1] 12  3
## 
## [1] 
## [1]
data(cornelius.maize)
mixed.res <- standard.sensitivity.plot(cornelius.maize,
                                     response = "yield",
                          TreatmentName = "gen",
                          TrialName = "env",
                          dual.dendrogram=TRUE,
                          plot.outliers=TRUE,legend.columns=3)
## Warning in anova.lm(base.lm): ANOVA F-tests on an essentially perfect fit
## are unreliable

print.stdplot(mixed.res)
## [1] AOV
## [1] ----------------------------------------------------
## Analysis of Variance Table
## 
## Response: yield
##            Df    Sum Sq  Mean Sq F value Pr(>F)
## env        19 247399973 13021051               
## gen         8  19960404  2495051               
## env:gen   152  62420142   410659               
## Residuals   0         0                        
## [1] Mixed Model
## [1] ----------------------------------------------------
## Linear mixed model fit by REML ['lmerMod']
## Formula: yield ~ env + (1 | env)
##    Data: plot.dat
## 
## REML criterion at convergence: 2602.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.08736 -0.61738 -0.00426  0.64595  2.76388 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  env      (Intercept) 2226600  1492.2  
##  Residual              514878   717.6  
## Number of obs: 180, groups:  env, 20
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   3612.7     1511.2   2.391
## envE02         596.8     2137.2   0.279
## envE03        1492.4     2137.2   0.698
## envE04        1617.4     2137.2   0.757
## envE05        1342.4     2137.2   0.628
## envE06        2692.8     2137.2   1.260
## envE07        -383.2     2137.2  -0.179
## envE08         415.1     2137.2   0.194
## envE09        1358.0     2137.2   0.635
## envE10        -676.2     2137.2  -0.316
## envE11        1694.6     2137.2   0.793
## envE12        3904.0     2137.2   1.827
## envE13        2719.7     2137.2   1.272
## envE14        2439.4     2137.2   1.141
## envE15        1436.2     2137.2   0.672
## envE16        1793.3     2137.2   0.839
## envE17        1264.1     2137.2   0.592
## envE18         938.8     2137.2   0.439
## envE19        -960.4     2137.2  -0.449
## envE20        1224.8     2137.2   0.573
## 
## Correlation of Fixed Effects:
##        (Intr) envE02 envE03 envE04 envE05 envE06 envE07 envE08 envE09
## envE02 -0.707                                                        
## envE03 -0.707  0.500                                                 
## envE04 -0.707  0.500  0.500                                          
## envE05 -0.707  0.500  0.500  0.500                                   
## envE06 -0.707  0.500  0.500  0.500  0.500                            
## envE07 -0.707  0.500  0.500  0.500  0.500  0.500                     
## envE08 -0.707  0.500  0.500  0.500  0.500  0.500  0.500              
## envE09 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500       
## envE10 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE11 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE12 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE13 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE14 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE15 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE16 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE17 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE18 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE19 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE20 -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
##        envE10 envE11 envE12 envE13 envE14 envE15 envE16 envE17 envE18
## envE02                                                               
## envE03                                                               
## envE04                                                               
## envE05                                                               
## envE06                                                               
## envE07                                                               
## envE08                                                               
## envE09                                                               
## envE10                                                               
## envE11  0.500                                                        
## envE12  0.500  0.500                                                 
## envE13  0.500  0.500  0.500                                          
## envE14  0.500  0.500  0.500  0.500                                   
## envE15  0.500  0.500  0.500  0.500  0.500                            
## envE16  0.500  0.500  0.500  0.500  0.500  0.500                     
## envE17  0.500  0.500  0.500  0.500  0.500  0.500  0.500              
## envE18  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500       
## envE19  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## envE20  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500  0.500
##        envE19
## envE02       
## envE03       
## envE04       
## envE05       
## envE06       
## envE07       
## envE08       
## envE09       
## envE10       
## envE11       
## envE12       
## envE13       
## envE14       
## envE15       
## envE16       
## envE17       
## envE18       
## envE19       
## envE20  0.500
## [1] 
## [1] Stability
## [1] ----------------------------------------------------
##   Treatment     Slope   Intercept    Mean        SD           b
## 1         1 0.9814750  -151.06934 4617.10 1362.2027 -0.01852496
## 2         2 0.9649816   -84.74141 4603.30 1254.0401 -0.03501841
## 3         3 0.9540153   188.08481 4822.85 1206.9871 -0.04598473
## 4         4 1.3142584 -1166.98659 5217.90 1707.5318  0.31425845
## 5         5 1.2280664  -719.10125 5247.05 1565.8725  0.22806640
## 6         6 1.0574596   192.88517 5330.20 1348.6694  0.05745957
## 7         7 0.8575980   506.04604 4672.40 1153.8584 -0.14240201
## 8         8 0.5240001  1737.47020 4283.15  882.0533 -0.47599990
## 9         9 1.1181456  -502.58763 4929.55 1457.8590  0.11814559
##             Pb         bR2
## 1 0.8909193724 0.001073705
## 2 0.7110067554 0.007810197
## 3 0.5384706179 0.021380030
## 4 0.0230407710 0.255349942
## 5 0.0379595028 0.217969243
## 6 0.5214449913 0.023203364
## 7 0.1768890353 0.098897030
## 8 0.0009673339 0.462629361
## 9 0.2980285266 0.059977186
## [1] 
## [1] Tukey's 1 d.f.
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = as.formula(modelString), data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1706.15  -298.66    -8.26   359.07  2113.48 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.372e+03  2.359e+02  14.293  < 2e-16 ***
## genG2       -1.380e+01  1.891e+02  -0.073 0.941930    
## genG3        2.058e+02  1.891e+02   1.088 0.278384    
## genG4        6.008e+02  1.891e+02   3.177 0.001807 ** 
## genG5        6.300e+02  1.891e+02   3.331 0.001089 ** 
## genG6        7.131e+02  1.891e+02   3.770 0.000233 ***
## genG7        5.530e+01  1.891e+02   0.292 0.770389    
## genG8       -3.339e+02  1.891e+02  -1.766 0.079465 .  
## genG9        3.124e+02  1.891e+02   1.652 0.100605    
## envE02       5.968e+02  2.819e+02   2.117 0.035925 *  
## envE03       1.492e+03  2.819e+02   5.294 4.16e-07 ***
## envE04       1.617e+03  2.819e+02   5.737 5.10e-08 ***
## envE05       1.342e+03  2.819e+02   4.761 4.47e-06 ***
## envE06       2.693e+03  2.819e+02   9.551  < 2e-16 ***
## envE07      -3.832e+02  2.819e+02  -1.359 0.176098    
## envE08       4.151e+02  2.819e+02   1.472 0.143009    
## envE09       1.358e+03  2.819e+02   4.817 3.52e-06 ***
## envE10      -6.762e+02  2.819e+02  -2.398 0.017684 *  
## envE11       1.695e+03  2.819e+02   6.010 1.33e-08 ***
## envE12       3.904e+03  2.819e+02  13.847  < 2e-16 ***
## envE13       2.720e+03  2.819e+02   9.646  < 2e-16 ***
## envE14       2.439e+03  2.819e+02   8.652 6.97e-15 ***
## envE15       1.436e+03  2.819e+02   5.094 1.03e-06 ***
## envE16       1.793e+03  2.819e+02   6.361 2.27e-09 ***
## envE17       1.264e+03  2.819e+02   4.484 1.44e-05 ***
## envE18       9.388e+02  2.819e+02   3.330 0.001093 ** 
## envE19      -9.604e+02  2.819e+02  -3.407 0.000843 ***
## envE20       1.225e+03  2.819e+02   4.344 2.55e-05 ***
## egen:eenv    5.536e-04  1.142e-04   4.848 3.07e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 598.1 on 151 degrees of freedom
## Multiple R-squared:  0.8362, Adjusted R-squared:  0.8058 
## F-statistic: 27.53 on 28 and 151 DF,  p-value: < 2.2e-16
## 
##              Df    Sum Sq  Mean Sq F value   Pr(>F)    
## gen           8  19960404  2495051   6.975 8.28e-08 ***
## env          19 247399973 13021051  36.402  < 2e-16 ***
## egen:eenv     1   8406983  8406983  23.503 3.07e-06 ***
## Residuals   151  54013159   357703                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Heterogenous Slopes
## [1] ----------------------------------------------------
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1610.55  -334.46    15.47   324.12  2208.91 
## 
## Coefficients: (1 not defined because of singularities)
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.224e+03  2.697e+02  11.956  < 2e-16 ***
## genG2       -1.380e+01  1.880e+02  -0.073 0.941597    
## genG3        2.058e+02  1.880e+02   1.094 0.275688    
## genG4        6.008e+02  1.880e+02   3.195 0.001717 ** 
## genG5        6.300e+02  1.880e+02   3.350 0.001031 ** 
## genG6        7.131e+02  1.880e+02   3.792 0.000219 ***
## genG7        5.530e+01  1.880e+02   0.294 0.769109    
## genG8       -3.339e+02  1.880e+02  -1.776 0.077844 .  
## genG9        3.124e+02  1.880e+02   1.662 0.098756 .  
## envE02       6.673e+02  2.875e+02   2.321 0.021681 *  
## envE03       1.669e+03  3.225e+02   5.174 7.56e-07 ***
## envE04       1.809e+03  3.294e+02   5.491 1.76e-07 ***
## envE05       1.501e+03  3.149e+02   4.766 4.54e-06 ***
## envE06       3.011e+03  4.018e+02   7.493 6.26e-12 ***
## envE07      -4.285e+02  2.833e+02  -1.513 0.132571    
## envE08       4.642e+02  2.838e+02   1.636 0.104126    
## envE09       1.518e+03  3.157e+02   4.810 3.76e-06 ***
## envE10      -7.561e+02  2.895e+02  -2.612 0.009956 ** 
## envE11       1.895e+03  3.338e+02   5.677 7.30e-08 ***
## envE12       4.365e+03  5.028e+02   8.682 7.69e-15 ***
## envE13       3.041e+03  4.039e+02   7.529 5.13e-12 ***
## envE14       2.728e+03  3.829e+02   7.124 4.63e-11 ***
## envE15       1.606e+03  3.196e+02   5.024 1.47e-06 ***
## envE16       2.005e+03  3.396e+02   5.904 2.44e-08 ***
## envE17       1.413e+03  3.112e+02   4.542 1.17e-05 ***
## envE18       1.050e+03  2.977e+02   3.526 0.000567 ***
## envE19      -1.074e+03  2.985e+02  -3.597 0.000441 ***
## envE20       1.369e+03  3.094e+02   4.426 1.88e-05 ***
## genG1:eenv  -1.367e-01  1.604e-01  -0.852 0.395562    
## genG2:eenv  -1.532e-01  1.604e-01  -0.955 0.341201    
## genG3:eenv  -1.641e-01  1.604e-01  -1.023 0.307869    
## genG4:eenv   1.961e-01  1.604e-01   1.223 0.223428    
## genG5:eenv   1.099e-01  1.604e-01   0.685 0.494232    
## genG6:eenv  -6.069e-02  1.604e-01  -0.378 0.705714    
## genG7:eenv  -2.606e-01  1.604e-01  -1.624 0.106461    
## genG8:eenv  -5.941e-01  1.604e-01  -3.704 0.000301 ***
## genG9:eenv          NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 594.6 on 144 degrees of freedom
## Multiple R-squared:  0.8456, Adjusted R-squared:  0.8081 
## F-statistic: 22.53 on 35 and 144 DF,  p-value: < 2.2e-16
## 
## 
## Call:
## lm(formula = form, data = nonadditivity.res$multiplicative.lm$model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1610.55  -334.46    15.47   324.12  2208.91 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## genG1      4617.1000   125.3564  36.832  < 2e-16 ***
## genG2      4603.3000   125.3564  36.722  < 2e-16 ***
## genG3      4822.8500   125.3564  38.473  < 2e-16 ***
## genG4      5217.9000   125.3564  41.625  < 2e-16 ***
## genG5      5247.0500   125.3564  41.857  < 2e-16 ***
## genG6      5330.2000   125.3564  42.520  < 2e-16 ***
## genG7      4672.4000   125.3564  37.273  < 2e-16 ***
## genG8      4283.1500   125.3564  34.168  < 2e-16 ***
## genG9      4929.5500   125.3564  39.324  < 2e-16 ***
## genG1:eenv    0.9815     0.1069   9.179  < 2e-16 ***
## genG2:eenv    0.9650     0.1069   9.025 5.03e-16 ***
## genG3:eenv    0.9540     0.1069   8.922 9.36e-16 ***
## genG4:eenv    1.3143     0.1069  12.291  < 2e-16 ***
## genG5:eenv    1.2281     0.1069  11.485  < 2e-16 ***
## genG6:eenv    1.0575     0.1069   9.890  < 2e-16 ***
## genG7:eenv    0.8576     0.1069   8.020 2.00e-13 ***
## genG8:eenv    0.5240     0.1069   4.901 2.30e-06 ***
## genG9:eenv    1.1181     0.1069  10.457  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 560.6 on 162 degrees of freedom
## Multiple R-squared:  0.9889, Adjusted R-squared:  0.9876 
## F-statistic: 800.3 on 18 and 162 DF,  p-value: < 2.2e-16
## 
##            Df    Sum Sq   Mean Sq F value Pr(>F)    
## gen         9 4.268e+09 474253490 1508.99 <2e-16 ***
## gen:eenv    9 2.589e+08  28767336   91.53 <2e-16 ***
## Residuals 162 5.091e+07    314285                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##              Df    Sum Sq  Mean Sq F value   Pr(>F)    
## gen           8  19960404  2495051   7.057 7.62e-08 ***
## env          19 247399973 13021051  36.827  < 2e-16 ***
## gen:eenv      8  11506049  1438256   4.068 0.000217 ***
## Residuals   144  50914093   353570                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 
## [1] Random Outliers
## [1] ----------------------------------------------------
## [1] Critical Value:
## [1] 1928.835
## [1] Interaction sd Value:
## [1] 642.9451
## [1] Error sd Value:
## [1] NaN
## [1] Pairs:
## [[1]]
## [1] 1 8
## 
## [1] 
## [1]
tdf.tbl <- anova(mixed.res$tdf$multiplicative.lm)
anova(mixed.res$tdf$multiplicative.lm)
## Analysis of Variance Table
## 
## Response: yield
##            Df    Sum Sq  Mean Sq F value    Pr(>F)    
## gen         8  19960404  2495051  6.9752 8.283e-08 ***
## env        19 247399973 13021051 36.4018 < 2.2e-16 ***
## egen:eenv   1   8406983  8406983 23.5027 3.070e-06 ***
## Residuals 151  54013159   357703                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(mixed.res$tdf$multiplicative.lm)
## 
## Call:
## lm(formula = as.formula(modelString), data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1706.15  -298.66    -8.26   359.07  2113.48 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.372e+03  2.359e+02  14.293  < 2e-16 ***
## genG2       -1.380e+01  1.891e+02  -0.073 0.941930    
## genG3        2.058e+02  1.891e+02   1.088 0.278384    
## genG4        6.008e+02  1.891e+02   3.177 0.001807 ** 
## genG5        6.300e+02  1.891e+02   3.331 0.001089 ** 
## genG6        7.131e+02  1.891e+02   3.770 0.000233 ***
## genG7        5.530e+01  1.891e+02   0.292 0.770389    
## genG8       -3.339e+02  1.891e+02  -1.766 0.079465 .  
## genG9        3.124e+02  1.891e+02   1.652 0.100605    
## envE02       5.968e+02  2.819e+02   2.117 0.035925 *  
## envE03       1.492e+03  2.819e+02   5.294 4.16e-07 ***
## envE04       1.617e+03  2.819e+02   5.737 5.10e-08 ***
## envE05       1.342e+03  2.819e+02   4.761 4.47e-06 ***
## envE06       2.693e+03  2.819e+02   9.551  < 2e-16 ***
## envE07      -3.832e+02  2.819e+02  -1.359 0.176098    
## envE08       4.151e+02  2.819e+02   1.472 0.143009    
## envE09       1.358e+03  2.819e+02   4.817 3.52e-06 ***
## envE10      -6.762e+02  2.819e+02  -2.398 0.017684 *  
## envE11       1.695e+03  2.819e+02   6.010 1.33e-08 ***
## envE12       3.904e+03  2.819e+02  13.847  < 2e-16 ***
## envE13       2.720e+03  2.819e+02   9.646  < 2e-16 ***
## envE14       2.439e+03  2.819e+02   8.652 6.97e-15 ***
## envE15       1.436e+03  2.819e+02   5.094 1.03e-06 ***
## envE16       1.793e+03  2.819e+02   6.361 2.27e-09 ***
## envE17       1.264e+03  2.819e+02   4.484 1.44e-05 ***
## envE18       9.388e+02  2.819e+02   3.330 0.001093 ** 
## envE19      -9.604e+02  2.819e+02  -3.407 0.000843 ***
## envE20       1.225e+03  2.819e+02   4.344 2.55e-05 ***
## egen:eenv    5.536e-04  1.142e-04   4.848 3.07e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 598.1 on 151 degrees of freedom
## Multiple R-squared:  0.8362, Adjusted R-squared:  0.8058 
## F-statistic: 27.53 on 28 and 151 DF,  p-value: < 2.2e-16

Working backwards, standard error for genotype estimates, the heterogeneous null model, is 560.6/sqrt(20), but I still haven’t worked out the SE for heterogeneous slopes. Still, if we assume balanced, then we can use the given standard error

sqrt(357703)/sqrt(8406983)

ee <- mixed.res\(tdf\)multiplicative.lm\(model\)egenmixed.res\(tdf\)multiplicative.lm\(model\)eenv > sum(eeee) [1] 2.743446e+13 > sqrt(357703/2.743446e+13) [1] 0.0001141861

SE = sb1 = sqrt [ Σ(yi - ŷi)2 / (n - 2) ] / sqrt [ Σ(xi - x)2 ]